The Role of Python in Developing Artificial Intelligence Applications: A Review of Tools, Libraries, and Learning Accessibility

Python has emerged as a leading programming language for artificial intelligence because of its readable syntax, extensive library ecosystem, open-source nature, and accessibility. This review examines the factors driving Python’s adoption in AI, its principal libraries and frameworks, major applications, and limitations compared with alternative languages. Tools such as NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras, and OpenCV support data processing, machine learning, deep learning, and computer vision. Python is widely applied in natural language processing, healthcare, bioinformatics, education, finance, automation, and emerging technologies. The review finds that Python’s primary strength lies in simplifying development, enabling rapid prototyping, supporting collaboration, and reducing barriers to AI learning. Although C++, Java, and R may provide advantages in execution speed, enterprise performance, or statistical analysis, Python compensates through optimized libraries and strong community support. Despite challenges involving memory use, security, scalability, and package dependencies, Python is likely to remain central to AI development.