Student retention remains one of the most critical concerns for educational institutions worldwide. Increasing dropout rates negatively affect institutional reputation, funding, and student outcomes. Predictive analytics has emerged as a powerful approach for identifying students at risk of academic failure or withdrawal, enabling timely interventions and support mechanisms. This study investigates the role of predictive analytics in enhancing student retention and academic success through the application of machine learning algorithms and educational data mining techniques. A simulated dataset consisting of 5,000 students was analyzed using Logistic Regression, Random Forest, and Gradient Boosting models. Results indicate that predictive models achieved accuracy rates exceeding 88%, with Random Forest demonstrating superior performance. Key predictors included attendance rates, prior academic achievement, engagement in learning management systems, socioeconomic background, and participation in extracurricular activities. The findings suggest that predictive analytics can significantly improve institutional decision-making and student support services. Ethical considerations, implementation challenges, and future developments in artificial intelligence-driven educational analytics are also discussed.