This review research paper aims to explore the potential of Artificial Intelligence (AI) and Machine Learning (ML) technologies in revolutionizing education and improving teaching and learning outcomes. It seeks to examine the theoretical framework, design and methodology, findings, implications, and originality of studies that have explored the integration of AI-ML technologies in educational settings. The study reviews existing literature on the theoretical underpinnings of AI-ML technologies in education. It investigates the concepts of personalized learning, adaptive assessment, intelligent tutoring systems, and data-driven decision-making to understand the theoretical foundations that support the integration of AI-ML in teaching and learning. A comprehensive review of relevant research articles, conference papers, and books is conducted. The selected studies are critically analyzed to identify trends, methodologies, and key findings related to the use of AI-ML technologies in education. The review encompasses a range of educational levels, from primary to higher education, and considers diverse subject areas. The findings reveal that the integration of AI-ML technologies in education has the potential to enhance teaching and learning outcomes. AI-ML can support personalized learning experiences, facilitate adaptive assessment and feedback, enable intelligent tutoring and mentoring, and assist educators in data-driven decision-making processes. The reviewed studies highlight improved student engagement, enhanced academic performance, and increased efficiency in instructional delivery as notable outcomes of AI-ML integration. This research paper discusses the implications of leveraging AI-ML technologies in education from multiple perspectives. It emphasizes the need for further research to address ethical considerations, data privacy, and the potential impact on teacher roles and professional development. The practical implications include guidance for educators and policymakers on effectively incorporating AI-ML technologies in educational practices, as well as recommendations for optimizing student support systems. This review research paper contributes to the field of education by consolidating and synthesizing the existing body of knowledge on the integration of AI-ML technologies. It provides insights into the theoretical foundations, design considerations, and practical implications of utilizing AI-ML to enhance teaching and learning outcomes. The paper highlights the originality and value of this research in guiding future studies and informing educational stakeholders on the transformative potential of AI-ML in education.