Artificial Intelligence and Machine Learning: From Historical Development to Modern Algorithms

This review provides a comprehensive overview of artificial intelligence and machine learning, covering their historical development, theoretical foundations, major algorithm families, practical applications, advantages, limitations, and future perspectives. The article discusses machine learning, deep learning, artificial neural networks, expert systems, genetic algorithms, fuzzy logic, support vector machines, convolutional and recurrent neural networks, clustering methods, and other widely used AI techniques. It also examines the impact of artificial intelligence on healthcare, education, business, finance, and future technologies while highlighting ethical considerations, interpretability, and responsible AI development. The review is intended as a structured, citation-supported introduction for researchers, students, and professionals interested in modern artificial intelligence.