Artificial Intelligence (AI) is transforming education through adaptive learning systems that personalize instructional content based on student performance, behaviors, and learning needs. This systematic review synthesizes recent advancements in AI-powered adaptive learning systems, focusing on the application of machine learning, reinforcement learning, natural language processing, and Bayesian knowledge tracing. Based on a corpus of 64 peer-reviewed studies, we analyze publication trends and effectiveness studies from the last decade, showing a consistent increase in empirical validation and implementation across diverse educational contexts. Ethical challenges, including algorithmic bias, data privacy, and explainability, are also addressed. Finally, we explore the emerging role of Agentic AI in supporting autonomous, context-aware, and self-improving learning experiences. This review offers insights for researchers and educators seeking to understand the evolving landscape of AI-enhanced personalized education.