We have all seen the immense potential of AI in multiple fields these days. From using natural language processing (NLP) to introducing intelligent AI assistants and chatbots, to OpenAI’s Dall-E, which generates images from text, AI is here to stay and rule.
Many beginners are enthusiastic about getting into AI-related fields. Before delving into AI, choosing the first step is very important, which is selecting the right programming languages used for AI development.
In this blog, we will explore which programming language is used for artificial intelligence applications and expert systems, their top libraries and packages, and their roles in AI development.
Top AI Programming Languages in 2026
1. Python
Python offers analytics capabilities and data visualization, which makes it an ideal programming language for AI development. It is beginner-friendly as it has a simple syntax that data analysts and scientists can easily learn. Due to libraries like NumPy, Python offers comprehensive support for scientific computing and matrices.
How is Python used in AI?
Python is used in AI for computer vision applications, ML systems, NLP, and AI prototyping. It provides comprehensive applications in AI, ranging from predictive models, neural networks, deep learning, image recognition, face detection, chatbots, document analysis, to developing machine learning algorithms and algorithm research.
AI roles that use Python:
- Data scientists
- Machine learning engineers
- NLP engineers
- Research scientists
- Computer vision engineers
Top Python AI libraries & packages:
- NumPy
- Pandas
- Matplotlib
- Scikit-learn
- TensorFlow
2. Java
Java programming is used in AI applications that integrate with current business systems and runtimes. It is highly popular due to its comprehensive features, like a huge ecosystem with extensive libraries, ample developers, cross-platform portability, and stable runtimes. These features allow developers to use Java to create and deploy AI applications that work across various software and hardware platforms.
How is Java used in AI?
Java is used for standalone AI agents and business software in which analytics are embedded. It is also used for performance tuning agents or intelligent predictive maintenance. Developers use the Java programming language to create conversational interfaces in chatbots and voice assistants.
AI roles that use Java
- Enterprise AI developer
- Android AI developer
- Big data engineer
- Software engineer
Top Java AI libraries & packages:
- WEKA
- DeepJAVA
- H2O
- Deeplearning4j (DL4J)
- Smile
3. R
It is popular for data science applications and is the most used language for statistical computing. R’s ecosystem accommodates data visualization and manipulation required for AI development.
How is R used in AI?
It is used in statistical machine learning, like Naive Bayes and random forest models. R is also used in data mining to generate association rules, cluster data, and decreases insights dimensions. R is mainly used for initial research and experimentation since it is unsuitable for later stages, like ML in real-life products.
AI roles that use R
- Data scientist
- Bioinformatics scientist
- AI researcher
- Econometrician (AI application in economics)
- Social data scientist
Top R AI libraries & packages:
- Tidyverse
- caret
- XGBoost
- randomForest
- IML (Interpretable Machine Learning)
4. Julia
Julia works well in both scenarios, when using multiple threads on one machine or across different machines. Thus, using it makes parallel programming and using multiple cores very fast. It works with existing R or Python codes.
How is Julia used in AI?
It is used for data science prototyping, and Julia’s computational powers are used in scientific simulations and models, computational biology research, time series analysis, and bioinformatics.
AI roles that use Julia
- AI engineer & researcher
- Robotics engineer
- Machine learning engineer
- Financial analyst
Top Julia AI libraries & packages:
- Flux.jl
- Mocha.jl
- DiffEq.jl
- StatsBase.jl
- Makie.jl
5. Mojo
Mojo programming language assists in making AI development efficient and easy with user code that preserves speed. It is still a new language in comparison to other programming languages, so it is yet to catch up when it comes to library support.
How is Mojo used in AI?
It simplifies the overall development process by bringing together Python’s usability and C++ and Rust’s performance. It can be merged with Python-based AI frameworks like TensorFlow and PyTorch. With this feature, AI researchers can use the existing model code and improve performance-critical areas.
AI roles that use Mojo
- Systems programmers
- AI and machine learning developers
- Python developers
- AI researchers
Top Mojo AI libraries & packages:
- Mojo standard library
- Linalg
- nn
- kv_cache
- quantization
- layout
- nvml
- tensor
6. Scala
It can merge functional programming and object-oriented to let modular data abstraction through methods, classes, and mathematical clarity. Also, Scala’s type system utilizes inference for flexibility and static checking for scale sturdiness.
How is Scala used in AI?
Scala is used to deploy machine learning into production at an elevated performance level. It is capable of real-time model serving and constructing streaming analytics pipelines. It can also be merged with big data ecosystems.
AI roles that use Scala
- Data scientist
- Spark developer
- AI researcher
- Machine learning developer
- Scala backend developer
Top Scala AI libraries & packages:
- Apache Spark
- Spark MLlib
- Breeze
- Deeplearning4j
- Scopt
7. Lisp
It is a high-performance programming language used in logical reasoning and rule-based AI applications. It can alter its code quickly through metaprogramming. Lisp denotes knowledge as code and data in the same symbolic tree structures. Lisp’s nested list code syntax makes this language simple to process and analyze. Today’s ML depends on this aspect heavily.
How is Lisp used in AI?
Lisp is popularly used in AI systems developed on complicated symbolic knowledge or logic. Examples are natural language processing (NLP), game-playing algorithms, logic programming, and automated reasoning.
AI roles that use Lisp
- AI language engineer and research scientist
- Open-source AI developer
- Legacy AI system maintainer
- AI educator
Top Lisp AI libraries & packages:
- Common Lisp User Environment(CL-USER)
- Applicative Common Lisp(ACL2)
- McAll
- OpenCyc
- Lisp implementations with AI functionalities
8. C++
C++ is a great option for AI model deployment into a low-latency production environment. This programming language can quickly perform machine learning programs using low memory. Thus, making it a great option for AI projects that need huge processing power. Its code can also be assembled into standalone executable programs across all operating systems and chips like Intel.
How is C++ used in AI?
C++ is used in embedded appliances, high-frequency trading algorithms, and autonomous robotics. C++ is known for its speed, which is why it can be used in production areas operating large-scale or latency-sensitive inferencing.
AI roles that use C++
- HPC AI engineer
- Robotics engineer
- Game AI programmer
- Embedded systems engineer
- Computer vision engineer
Top C++ AI libraries & packages:
- Eigen
- Armadillo
- Dlib
- OpenCV
- TensorFlow C++ API
9. JavaScript
JavaScript is used for comprehensive AI integration on web platforms. It utilizes an event-powered model to handle user inputs in real-time and update pages without delay. It can quickly prototype code, and its types are dynamic.
How is JavaScript used in AI?
JavaScript can sanction complicated features of ML in the browser, e.g., can analyze images and voice from the user without requiring any backend calls. You can build AI-powered interfaces across Android, iOS, and web from one codebase.
AI roles that use JavaScript
- Front-end web developer
- Conversational AI developer
- Web AI developer
- Full-stack AI developer
- AI visualization developer
Top JavaScript AI libraries & packages:
- TensorFlow.js
- Brain.js
- Synaptic.js
- Keras.js
- Ml5.js
10. Haskell
It is a pure functional language that uses mathematical functions for AI algorithms. Haskell is highly efficient as it works slowly, only performing calculations when absolutely needed. It also simplifies the declaration and abstraction of reusable AI components.
How is Haskell used in AI?
It is a great option for AI systems developed on symbolism and logic, like constraint programming, probabilistic modeling, combinatorial search, and proving theorems. It has ethical foundations and strong data types, which help to provide accuracy and flexibility to mathematical AI.
AI roles that use Haskell
- Research scientist
- AI researcher
- Domain-specific language developer
- Functional verification engineer
Top Haskell AI libraries & packages:
- HLearn
- Bayesbayes
- Halcyon
- BioCondor
Conclusion
I hope you have understood which programming language is used for artificial intelligence applications and expert systems by now. If you are a beginner, AI programming has huge potential today and in the upcoming years. You can definitely learn one of the many AI programming languages and open new doors towards a brighter and enriching career.
Related: Top 10 Blockchain Technology Trends For 2025
Related: How Blockchain Locks Data: A Beginner’s Guide
