Paper Contents
Abstract
Artificial Intelligence (AI) has become an indispensable driver of innovation across multiple sectors, with Machine Learning (ML) forming the backbone of this transformation. The convergence of AI and ML has redefined computational intelligence, enabling machines to learn from data, adapt to new information, and perform complex tasks with minimal human intervention. While AI provides the overarching framework for simulating human-like intelligence, ML equips systems with the ability to improve performance through experience. Over the past decade, rapid advancements in deep learning, natural language processing (NLP), reinforcement learning, and computer vision have accelerated the adoption of AI-driven ML systems in healthcare, finance, education, and autonomous technologies 12. This paper provides a comprehensive review of the evolution, methodologies, applications, and challenges of AI in ML. It highlights how AI techniques enhance ML models, examines state-of-the-art frameworks, and explores their real-world applications. Additionally, this review discusses the ethical, legal, and societal concerns associated with AI-powered ML systems and outlines future prospects such as explainable AI, quantum computing integration, and sustainable AI models. By synthesizing knowledge across academic and industrial domains, this review aims to provide a structured understanding of the role of AI in shaping the next generation of machine learning technologies.
Copyright
Copyright © 2025 Nikhil Sanjay Patil . This is an open access article distributed under the Creative Commons Attribution License.
