The integration of Artificial Intelligence (AI) into education is reshaping traditional teaching models by enabling unprecedented levels of personalized learning. In conventional classrooms, educators often face the challenge of addressing diverse student needs within uniform curricula. AI offers a transformative solution by tailoring content, pace, and instructional strategies to the unique cognitive profiles, preferences, and performance patterns of individual learners. Through adaptive learning algorithms, intelligent tutoring systems, natural language processing, and predictive analytics, AI facilitates a more responsive and learner-centric educational experience. This paper explores the multi-dimensional impact of AI on personalized education. It analyzes case studies and global implementations of AI-powered tools such as Squirrel AI in China, Mindspark in India, and adaptive platforms like DreamBox Learning in the United States. The findings highlight key benefits including improved academic performance, increased engagement, early identification of learning gaps, and enhanced teacher productivity. However, the deployment of AI in education also presents complex challenges—ranging from data privacy and algorithmic bias to digital inequity and the ethical implications of automation in pedagogy. Using a qualitative meta-analysis of over 90 peer-reviewed studies, policy documents, and EdTech deployments, this article critically evaluates the pedagogical, technological, and ethical dimensions of AI-driven personalization. The conclusion underscores the need for inclusive policy frameworks, teacher training, algorithmic transparency, and human-centered design to ensure that AI serves as a tool for equity rather than exclusion. With deliberate and responsible implementation, AI holds the potential to transform education into a truly personalized, inclusive, and empowering experience for all learners.