The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) technologies is reshaping educational landscapes worldwide. Personalized learning, characterized by tailored educational experiences based on individual student needs, preferences, and performance, is one of the most promising applications of these technologies. This paper explores the theoretical, technical, and practical aspects of integrating AI and ML into educational systems to create adaptive, student-centered learning environments. We begin by examining the foundational theories that link AI/ML to cognitive and instructional science. The paper then defines personalized learning and analyzes its necessity in 21st-century classrooms. Various applications, such as intelligent tutoring systems, recommendation engines, automated feedback systems, and predictive analytics, are presented as case studies. Benefits of AI-based personalization—such as improved student engagement, better learning outcomes, and optimized teaching strategies—are examined alongside challenges including data privacy, algorithmic bias, infrastructure disparities, and resistance to technological change. Furthermore, the study investigates real-world implementations of AI in diverse contexts, including K-12 education, higher education, and online learning platforms. Ethical implications and policy frameworks are addressed to ensure that AI-driven personalization is inclusive, transparent, and equitable. Finally, the article identifies current research gaps and proposes future directions for educators, developers, and policymakers. This comprehensive review underscores the transformative potential of AI and ML in fostering individualized education, while cautioning against unchecked adoption without critical evaluation of pedagogical and ethical consequences.