Improving educational quality requires attention to factors such as educational content, teaching methods, the quality of instructors, and the educational environment. Previous research has shown that interactive teaching methods, up-to-date and relevant content, and a suitable educational environment can have a significant impact on the quality of education. Machine learning can analyze educational data to identify student learning patterns and provide personalized recommendations that improve their performance and motivation. Numerous studies in this field have examined the role of machine learning in enhancing educational quality, but there has not been a comprehensive review of the literature in this area. This study aims to review previous research in the field under review and demonstrates that most of these studies focus on the benefits of machine learning without thoroughly examining its challenges and limitations. Among the challenges in this area, particularly with regard to improving educational quality in military academies, are issues related to data privacy, the need for advanced technological infrastructure, and the potential resistance of instructors and students to adopting new technologies. Additionally, the cultural and social impacts of using machine learning in military academies have received less attention. The main objective of this research is to identify and fill these research gaps and provide solutions for the optimal use of machine learning in improving educational quality. By offering practical recommendations and solutions, this research can assist decision-makers and policymakers in developing more effective programs and strategies to enhance educational quality.