Last updated on February 10th, 2025
Quick Summary:
Keeping pace with the acceleration of modern AI programming can be of prime significance for any developer aiming to break through into 2025. In this blog, our focus will be on outlining the top 20 AI programming languages to learn, their characteristics, advantages, and benefits to innovations driven by AI aid businesses and developers in making the right decisions regarding applications to use in the future.
Artificial Intelligence (AI) is changing like a wildfire and revolutionizing every field, including the unimaginative ways we influence technology. AI influences a wide range of areas, from OpenAI DALL-E producing vivid and colorful artistic depictions of textual input to DeepMind deeply influencing and paving the way for solving health-related issues throughout time. While businesses and developers scramble to adapt and harness the power of AI, one consideration rises above them all: picking the best AI programming languages.
There are plenty of options when it comes to choosing a language for AI development; it’s complex beyond measure. Python dominates with its extensive libraries, yet Java, C++, and Julia are steadily gaining their firm footholds in AI domains. The correct object very much depends on performance, scalability, and the ability to affect integration.
In this blog post, we will discover the top 20 AI programming languages in the year 2025 to give an insight into their strengths, use cases, and importance in constantly changing AI dynamics.
What is a Programming Language?
A programming language is a set of rules, syntax, and instructions that allow humans to communicate with computers. It acts as a median between human logic and machine execution—a developer can write code that computers can understand and process.
There are various kinds of programming languages, from higher-level languages that include Python and Java, among others, friendly to users who are abstracted from hardware up to the point of low-level languages like C and Assembly with direct control on system resources, each language giving its own distinct strengths that render some languages for AI, web development, and game design data science.
In AI development, programming languages do play a more important role regarding the handling of machine learning models, processing very large amounts of data, and executing complex algorithms efficiently. Hence, the right choice of the language depends mainly on speed, library support, and ease of use.
Top 20 AI Programming Languages for 2025
Choosing the right programming language is crucial for AI development. Each language offers unique capabilities, whether in speed, flexibility, or specialized libraries for machine learning and deep learning. Below, we explore the top 20 AI programming languages for 2025, their strengths, and how they are used in AI applications.
1. Python – Top Choice AI Programming Language
Python is a high-level interpreted programming language for AI development that is indeed easy to learn and read. It emphasizes code readability, which makes it an ideal first language for beginners and a handy tool for seasoned developers. Python is versatile enough to perform many tasks—from web development to automation to data science and machine learning—with its dynamic typing, huge standard library, and endless third-party modules.
AI Use:
Owing to NumPy, Pandas, scikit-learn, and TensorFlow—the powerful ecosystem of libraries that shaped its dominance in the AI space—Python provides tools for manipulating data through machine learning and deep learning. With Python, developers can quickly implement complex algorithms, process big data, and deploy models into production. Python’s popularity in AI is enhanced by its active community and vast number of tutorials, making it accessible for developers to build everything from small-scale models to large-scale AI applications.
| Pros | Cons |
| Easy to learn and use | Slower execution speed |
| Extensive AI/ML libraries (TensorFlow, PyTorch, Scikit-learn) | High memory consumption |
| Large community and strong support | Not ideal for mobile applications |
| Versatile for AI, web, and automation | GIL (Global Interpreter Lock) limits multi-threading performance |
2. R – AI Programming Language for Machine Learning & AI Development
R can be defined as a language that is predominantly built for statistical computation and visualization. It has many libraries that offer data manipulation and statistical modeling that are very popular among statisticians and data scientists. The syntax of R is in favor of data analysis, meaning it suits the needs of all those working on complex data sets, especially the researchers or practitioners.
AI Use:
R is primarily used in AI for statistical data analysis, exploratory data analysis (EDA), and building machine learning models. It has specialized packages such as caret for model training and evaluation and randomForest for building decision tree-based models. R’s visualization capabilities, such as ggplot2, are also used for data visualization in AI to help present model insights clearly. It is often preferred in research-heavy AI projects, especially in areas that require rigorous statistical validation.
| Pros | Cons |
| Excellent for statistical computing and data analysis | Slower execution speed compared to Python |
| Strong data visualization libraries (ggplot2, Shiny) | Steep learning curve for beginners |
| Good for machine learning applications | Not as widely used for general AI applications |
| Active community in the data science field | Less support for large-scale AI applications |
Must Check: Is R Better Than Power BI? An In-Depth Guide
3. Java – AI Programming Language: Best Option for Developers
Java is, undoubtedly, a powerful and object-oriented programming language, which is popularly known for its “write once, run anywhere” impulse because it is platform-independent via the Java Virtual Machine (JVM). It provides great scalability, maintainability, and performance for enterprise applications, which is why more enterprise-level solutions are built in it than in any other language.
AI Use:
AI systems have subsequently been built using Java for large-scale enterprise-level development. Because of wide-ranging support in text data processing, such as Apache OpenNLP and Stanford NLP, it has been applied to speech recognition, sentiment analysis, and language translation. Java libraries, such as Deeplearning4j, can build deep learning algorithms. Weka can be applied to a variety of machine learning algorithms. Java’s upscaling makes it a good fit for building AI due to the expected volume of data flow and response requirements of an AI system.
| Pros | Cons |
| Platform-independent (WORA – Write Once, Run Anywhere) | More verbose syntax compared to Python |
| Strong performance and scalability | Slower than C++ for computational tasks |
| Robust security features | Requires more memory and CPU resources |
| Large ecosystem with AI frameworks (Deeplearning4j, Weka) | Steeper learning curve for AI development |
4. Julia – AI Programming Language for Building Smart Applications
Julia is a high-performance, high-level programming language designed specifically for scientific computing and numerical tasks. Its speed is comparable to lower-level languages like C, while retaining ease of use similar to Python. Julia excels in handling heavy mathematical computations, making it suitable for numerical and scientific work.
AI Use:
Julia is increasingly popular in AI due to its exceptional performance in large-scale numerical computations. In machine learning and data science, Julia’s ability to handle complex mathematical operations efficiently enables faster model training. Julia is favored for AI research, particularly in academic settings, due to its ability to handle tasks like data analysis, simulations, and optimization with high performance. Frameworks like Flux.jl and Knet.jl are rapidly gaining traction for deep learning tasks in Julia.
| Pros | Cons |
| High performance for numerical computing | Smaller community compared to Python |
| Simple and readable syntax | Fewer AI/ML libraries available |
| Designed for scientific computing and AI | Not widely used in production AI applications |
| Supports parallel computing efficiently | Limited job market compared to Python and Java |
5. JavaScript – AI Programming Language: Powering the Future of AI
JavaScript is primarily a scripting language that is widely used for the development of websites. It creates dynamic and interactive content on a website and constitutes the heart of client-side scripting for web application development. This feature, among others, coupled with its strong ecosystem, helps build responsive, scalable, and very responsive web applications.
AI Use:
The rapid growth of demand for web applications that are powered by AI has pushed JavaScript to the forefront in AI development. For instance, libraries like TensorFlow.js allow for the deployment of machine learning models directly inside the browser and hence could be utilized to create real-time AI applications, such as image recognition, recommendation systems, and NLP-based chatbots, completely without server-side processing. JavaScript’s ability to run on both the client and the server (with the help of Node.js) allows developers to use machine learning models organically within web applications.
| Pros | Cons |
| Runs in the browser, making AI integration with web apps easy | Not suitable for heavy AI computations |
| Supports TensorFlow.js for machine learning in web apps | Limited AI/ML libraries compared to Python |
| Wide adoption and active developer community | Lacks built-in multi-threading support |
| Works well for AI-powered chatbots and web automation | Slower than Python and C++ for AI processing |
6. C++ – AI Programming Language for Deep Learning & Data Science
C++ is an efficiency-oriented, compiled language offering a low-level approach to accessing memory and other resources of the computer system, with applications to real-time applications requiring performance at maximum levels.
AI Use:
C++ is widely utilized in AI in high-performance applications of real time. This involves developing game AI, robotics, and image processing, among other uses. It finds particular usage in AI to benefit applications where the need is to have lower latency and stronger computing power, like data analysis on a real-time basis in robots or computer vision systems. For image processing, the OpenCV library is being utilized, whereas TensorFlow C++ API is a well-known developer API for AI model building that facilitates optimization towards utilization in critical environments and missions.
| Pros | Cons |
| High performance and low-level memory control | Complex syntax with a steep learning curve |
| Used in AI applications requiring speed (e.g., robotics, game AI) | Manual memory management can be challenging |
| Supports multi-threading for parallel processing | Slower development time compared to Python |
| Libraries like OpenCV and TensorFlow have C++ support | Not ideal for beginners in AI programming |
7. Lisp – AI Programming Languages: Essential for AI Engineers
Lisp is one of the oldest programming languages, created in the 1950s. It is a functional language known for its unique parenthetical syntax and its strong support for symbolic reasoning. Lisp’s macro system allows developers to extend the language’s functionality, making it powerful for AI tasks that involve logic and computation.
AI Use:
One of the first languages used in researching artificial intelligence was Lisp, particularly into symbolic reasoning based on knowledge representation and rule-based problem solving. Although no longer used in mainstream AI, it continues to find application in such areas as expert systems and automated theorem proving. Because of its facilities in handling symbolic reasoning—a feature still sought for unique applications in academia and AI research—Lisp remains a timeless choice.
| Pros | Cons |
| Great for symbolic reasoning and AI research | Not widely used in modern AI applications |
| Supports rapid prototyping and dynamic programming | Limited libraries and frameworks compared to Python |
| Powerful for developing AI rule-based systems | Smaller developer community |
| Strong flexibility in AI program manipulation | Learning curve is steep for new programmers |
8. Haskell – AI Programming Language for Scalable AI Solutions
Haskell is a purely functional programming language that emphasizes immutability, higher-order functions, and strong typing. It is known for its mathematical foundations, making it well-suited for applications requiring formal reasoning and correctness.
AI Use:
In AI, Haskell is used for research in functional programming paradigms, symbolic AI, and formal methods. Its strong typing system and support for lazy evaluation make it ideal for creating AI models that require mathematical rigor and efficiency. Haskell has been used in building domain-specific languages for AI, and its focus on immutability and high-level abstractions makes it a choice for AI researchers working on complex algorithms or systems that require correctness and proof.
| Pros | Cons |
| Strong in functional programming and AI logic applications | Small ecosystem for AI development |
| High level of abstraction and efficiency | Not beginner-friendly |
| Good for formal verification in AI | Lack of AI-specific libraries |
| Type-safe and concise syntax | Slower adoption in mainstream AI applications |
9. Prolog – AI Programming Language: Driving Innovation in AI Development
Prolog is a declarative programming language that is based on formal logic. It allows developers to define facts and rules that a computer can use to infer information. Prolog excels in symbolic reasoning, making it useful for applications requiring logical deduction or rule-based systems.
AI Use:
Prolog is widely used in AI for tasks that involve symbolic reasoning, such as expert systems, natural language understanding, and automated theorem proving. Its syntax and structure are highly conducive to building rule-based systems that mimic human reasoning. In AI, Prolog is used in domains such as knowledge representation, decision-making, and problem-solving, where logical inference and reasoning are needed.
| Pros | Cons |
| Ideal for logic-based AI applications | Not widely used in commercial AI projects |
| Great for natural language processing (NLP) and expert systems | Slower execution speed |
| Simple syntax for AI rule-based modeling | Limited ecosystem for modern AI applications |
| Used in AI research and automated reasoning | Fewer job opportunities in AI development |
10. Scala – AI Programming Language for Advanced Neural Networks
Scala is a hybrid programming language; through the fusion of functional and object-oriented paradigms, Scala is an alphabet-plus language that runs on the JVM. It is well-known for its concise syntax and has high scalability. Scala has traditionally been popular for big data processing and distributed systems through its support for functional programming and its interoperability with Java.
AI Use:
Not only very scalable but also able to adapt easily to multithreading for big data research and AI. Scala has a matured framework for machine learning because of the functional programming paradigm and support for big data, including Apache Spark and Hadoop. Scala applications may take advantage of an expressive and powerful syntax that does not compromise the scalability of the application.
| Pros | Cons |
| Combines object-oriented and functional programming | Steeper learning curve compared to Java |
| Runs on JVM, making it highly scalable | Not as widely adopted for AI as Python |
| Good interoperability with Java AI libraries | Slower than C++ for computation-heavy AI tasks |
| Supports parallel and distributed computing | Less AI-focused ecosystem |
11. Go (Golang) – AI Programming Language: The Foundation of AI Technology
Go, created by Google, is a statically typed and compiled language. This has been used as a building for highly concurrent systems and makes it more prominent for the backends, for applications on clouds, and where very high throughput systems are being considered.
AI Use:
Go is utilized in AI to build scalable back-end systems and deploy machine learning models efficiently. Its concurrency model is ideal for developing distributed AI systems and handling large-scale data processing. With its simplicity and performance, Go is a great choice for any AI application with a high request volume or with large datasets, such as the serving of models via APIs or handling data from IoT devices.
| Pros | Cons |
| Fast execution speed | Limited AI/ML libraries compared to Python |
| Efficient concurrency and parallel computing | Not widely used for AI development |
| Simple and easy-to-read syntax | Smaller AI developer community |
| Good for scalable backend AI services | Lacks built-in deep learning support |
Must Check: Top 15 Golang Web Frameworks for Developers
12. Swift – AI Programming Language for High-Performance AI Models
Swift is a modern, fast, and safe programming language developed by Apple for iOS and macOS development. It is known for its performance, safety features (like optional types and memory management), and ease of use. Swift allows developers to write efficient, expressive code for building apps on Apple platforms.
AI Use:
Swift is used in AI for the integration of machine learning models directly into iOS and macOS applications. The Core ML framework, developed by Apple, and tools like TensorFlow Swift allow developers to create high-performance machine learning models that run on Apple devices. This is particularly useful in mobile AI applications, such as image recognition, speech processing, and on-device prediction models.
| Pros | Cons |
| Optimized for Apple ecosystem (iOS, macOS AI applications) | Limited AI libraries and frameworks |
| Safe memory management and fast execution | Not widely used outside Apple platforms |
| Good interoperability with TensorFlow | Less AI-specific community support |
| Concise and modern syntax | Not suitable for large-scale AI projects |
13. MATLAB – AI Programming Language: Optimized for Automation & Robotics
MATLAB is a programming language and software environment for numeric computation, particularly suitable for operations that involve intense numerical computations as well as visualizing data, primarily used at academic and research levels because it contains extensive, in-built libraries on linear algebra, matrix, signal processing, and optimization.
AI Use:
MATLAB is used in AI for building and testing machine learning algorithms, particularly in signal processing, image processing, and control systems. It has a friendly interface and a rich set of toolboxes, which makes it the best prototyping environment for AI models. Researchers use MATLAB to quickly iterate on models before moving to more scalable languages for production use.
| Pros | Cons |
| Excellent for mathematical computing and simulations | Expensive and not open-source |
| Rich set of built-in AI and ML toolboxes | Slower execution speed compared to Python and C++ |
| Great for AI prototyping and academia | Not widely used in production AI applications |
| User-friendly interface | Limited community support compared to Python |
14. Ruby – AI Programming Language for Artificial Intelligence Startups
It’s a dynamic, high-level programming language that emphasizes ease of use and productivity; it has become very popular in web development with the Ruby on Rails framework allowing fast web application development. Object-oriented approach of Ruby helps to learn easily and use conveniently.
AI Use:
Ruby is used in AI primarily for developing web-based AI applications, using libraries like ai4r for machine learning tasks and other tools for text analysis and data processing. While Ruby is not typically the first choice for large-scale AI tasks, it offers a simple way to prototype machine learning solutions in the web domain.
| Pros | Cons |
| Simple and readable syntax | Limited AI libraries and frameworks |
| Good for AI prototyping and automation | Slower execution speed |
| Strong community support | Less popular for AI compared to Python |
| Works well with AI-powered web applications | Not optimized for large-scale AI processing |
15. Kotlin – AI Programming Language: The Backbone of AI Applications
Kotlin is statically typed and runs on the Java Virtual Machine, but it’s fully interoperable with Java. It’s very concise in syntax and has a lot of modern features, like null safety and extension functions. Kotlin is typically used for Android development and for backend services.
AI Use:
Kotlin is used in AI for developing Android applications with integrated machine learning models. It is also used in backend systems for handling data processing and model deployment. Kotlin’s modern features make it easier to work with AI libraries and tools, particularly when deploying models on Android or Java-based systems.
| Pros | Cons |
| Modern and concise syntax | Smaller AI ecosystem compared to Java |
| Interoperability with Java and Android AI apps | Not widely adopted for AI research |
| Runs on JVM, making it scalable | Fewer AI frameworks available |
16. Rust – AI Programming Language for Predictive Analytics & Big Data
Rust is a systems programming language that emphasizes memory safety without sacrificing performance. Its strong static typing system and emphasis on concurrency and parallelism make it suitable for performance-critical applications. Rust is increasingly used in high-performance computing and embedded systems.
AI Use:
Rust is used in AI to develop high-performance, secure AI systems, particularly for tasks where low-level control over memory and hardware is required. Rust’s safety features ensure that AI systems are less prone to bugs and security vulnerabilities. Rust is also used in areas like computer vision and robotics, where performance and safety are paramount.
| Pros | Cons |
| High performance and memory safety | Complex syntax for beginners |
| No garbage collection, making it efficient for AI computations | Smaller AI/ML library ecosystem |
| Good for AI systems requiring concurrency | Slower adoption in AI community |
Suggested: AI Frameworks & Libraries
17. TypeScript – AI Programming Language: Enabling Smarter AI Systems
TypeScript is a superset of JavaScript that adds static typing. It is designed for large-scale applications and improves code quality by catching errors during development rather than at runtime. TypeScript provides better tooling support than JavaScript, making it easier to maintain large projects.
AI Use:
TypeScript is used in AI for building scalable web-based applications that integrate machine learning models. It is commonly used in conjunction with TensorFlow.js, enabling AI capabilities directly in the browser. TypeScript’s strong typing and modern features make it easier to manage complex AI codebases, particularly for web developers working on large-scale AI projects.
| Pros | Cons |
| Adds type safety to JavaScript for AI-powered web applications | Not ideal for AI-heavy computations |
| Works well for AI-powered chatbots and automation | Fewer AI/ML libraries than Python |
| Strong community support | Not optimized for large-scale AI development |
18. Perl – AI Programming Language for Real-World AI Implementations
Perl is a versatile scripting language known for its powerful text processing capabilities. It is widely used in system administration, web development, and network programming. Perl is particularly well-suited for handling regular expressions and working with text-based data.
AI Use:
While not as commonly used in modern AI, Perl remains useful in specialized AI tasks such as natural language processing (NLP) and text mining. Its powerful text-processing capabilities make it a good choice for AI applications that involve parsing, manipulating, or analyzing large volumes of text data.
| Pros | Cons |
| Great for text processing and data extraction | Outdated for AI and ML applications |
| Used in bioinformatics and AI-related scripting | Fewer AI libraries and frameworks |
| Simple syntax for automation | Slower execution speed |
19. Dart – AI Programming Language: Revolutionizing AI & ML Development
Dart is an object-oriented, class-based language developed by Google, designed for building mobile, web, and desktop applications. It is the primary language for developing apps with the Flutter framework, which enables cross-platform development.
AI Use:
Dart is used in AI for developing mobile and cross-platform applications with integrated AI features. It is often used with Flutter for creating AI-powered apps that run seamlessly across different platforms, particularly in mobile AI use cases such as recommendation engines, voice assistants, and image recognition.
| Pros | Cons |
| Good for AI in mobile applications (Flutter) | Limited AI/ML libraries |
| Fast execution speed | Not widely adopted for AI |
| Supports asynchronous programming | Smaller developer community |
20. Elixir – AI Programming Language for the Next Generation of AI
Elixir is a functional, concurrent language built on the Erlang VM. It is designed for building highly scalable, fault-tolerant applications. Elixir’s concurrency model and lightweight processes make it an excellent choice for real-time systems and applications that require high availability.
AI Use:
While not a mainstream AI language, Elixir is useful for developing distributed AI systems that need to handle massive parallelism. Its fault tolerance and scalability make it an appealing option for building AI applications in domains such as real-time data processing, chatbots, and large-scale recommendation systems.
| Pros | Cons |
| High scalability and concurrency | Not widely used for AI applications |
| Fault-tolerant, ideal for AI-based distributed systems | Smaller ecosystem compared to Python |
| Functional programming benefits AI research | Limited AI frameworks |
AI Programming Languages: A Detailed Comparative Review
| Language | Ease of Use | Performance | AI Libraries/Frameworks | Primary AI Use Cases | Founding Date |
| Python | High | Moderate | TensorFlow, PyTorch | ML, DL, NLP, Data Science | 1991 |
| R | Moderate | Moderate | caret, randomForest | Statistical Modeling, Data Analysis | 1993 |
| Java | Moderate | High | Deeplearning4j, Weka | Large-scale Systems, NLP | 1995 |
| Julia | High | High | Flux, MLJ | Scientific Computing, ML | 2012 |
| JavaScript | High | Moderate | TensorFlow.js | Web-based AI, Browser ML | 1995 |
| C++ | Low | High | OpenCV, TensorFlow C++ | Game AI, Robotics, Computer Vision | 1985 |
| Lisp | Low | Moderate | CLOS, AllegroCache | Symbolic AI, Expert Systems | 1958 |
| Haskell | Low | High | HLearn, Grenade | Functional AI, Research | 1990 |
| Prolog | Low | Moderate | SWI-Prolog | Expert Systems, NLP | 1972 |
| Scala | Moderate | High | Apache Spark | Big Data, Distributed ML | 2004 |
| Go | High | High | Gorgonia, GoLearn | Scalable Backend Systems | 2009 |
| Swift | High | High | Core ML, TensorFlow Swift | iOS/macOS ML Applications | 2014 |
| MATLAB | High | Moderate | Deep Learning Toolbox | Prototyping, Signal Processing | 1984 |
| Ruby | High | Moderate | ai4r | Web-based AI, Scripting | 1995 |
| Kotlin | High | High | KotlinDL | Android AI, Backend Systems | 2011 |
| Rust | Moderate | High | RustML | High-performance AI Systems | 2010 |
| TypeScript | High | Moderate | TensorFlow.js | Web-based AI Applications | 2012 |
| Perl | Moderate | Moderate | AI::Perceptron | Text Processing, NLP | 1987 |
| Dart | High | High | TensorFlow Lite | Cross-platform AI Applications | 2011 |
| Elixir | Moderate | High | Nx | Distributed AI Systems | 2011 |
Conclusion:
Selecting the ideal AI programming languages depends on your specific project requirements and the problem you’re aiming to solve. While Python is widely recognized for its versatility and ease of use in AI development, languages like Java and Scala are more suited for large-scale, enterprise-level applications. If performance is a critical factor, C++ and Rust stand out for their high-speed capabilities, while Julia excels in handling complex mathematical computations.
It’s important to assess the unique needs of your AI application—whether it’s rapid prototyping, real-time performance, or web-based integration—and choose the language that best fits those needs. In some cases, combining multiple languages for different components of the system may offer the best results. Always prioritize maintainable, scalable solutions and consider the long-term goals of your project to ensure you’re equipped for future AI advancements.
FAQ’s For AI Programming Languages
Which programming language is best for AI?
The best programming language for AI depends on the project requirements. Python is a top choice due to its simplicity, vast libraries like TensorFlow and PyTorch, and strong community support. For performance-intensive tasks, C++ or Julia might be better. Java, R, and Scala are also great for specific use cases like data analysis or large-scale systems.
What language is used for AI?
AI development can be done in various languages depending on the task. Python is the most popular, supported by extensive libraries for machine learning and data analysis. Other languages include R for statistics, Java for enterprise systems, C++ for performance-critical applications, and Prolog for symbolic reasoning or logic-based AI systems.
Is AI coded in Python?
Yes, Python is widely used for AI development. It is favored for its simplicity, readability, and extensive libraries like TensorFlow, PyTorch, and scikit-learn. Python enables easy experimentation, rapid prototyping, and seamless integration with other tools, making it the go-to language for machine learning, deep learning, and natural language processing tasks.
Is C++ or Python better for AI?
Python is generally preferred for AI development due to its simplicity, rich ecosystem, and developer-friendly syntax. However, C++ is better for performance-critical AI applications like robotics and game AI. C++ provides faster execution, lower-level control, and greater efficiency but requires more complex code, making it less accessible for rapid AI development.
