The rapid advancement of Artificial Intelligence (AI) has fundamentally transformed educational systems worldwide, opening new avenues for personalized learning, intelligent tutoring, and data-driven decision-making. This paper presents a systematic literature review examining the current state of AI integration in education, covering the period from 2015 to 2026. Drawing on 155 peer-reviewed studies indexed in Scopus, Web of Science, IEEE Xplore, and Google Scholar, this review categorizes key AI technologies — including Machine Learning (ML), Natural Language Processing (NLP), Generative AI, and Intelligent Tutoring Systems (ITS) — and analyzes their applications across K-12, undergraduate, and postgraduate contexts. The methodology follows the PRISMA framework for systematic reviews. The analysis reveals significant benefits such as enhanced learning outcomes, personalized instruction, and increased student engagement, while also identifying persistent challenges including ethical concerns, digital inequality, teacher resistance, and lack of institutional guidelines. Statistical evidence shows that over 70% of experimental studies report measurable learning improvements, yet only 10% of institutions have formal AI policies. A structured implementation framework (AIEIM) is proposed to guide responsible AI adoption. The paper concludes with a synthesis of answers to four research questions and directions for future investigation.................... Keywords:....................Artificial Intelligence, Education, Machine Learning, Personalized Learning, Intelligent Tutoring Systems, Generative AI, Systematic Literature Review, PRISMA.