Organizations face significant difficulties when employees leave their positions, which brings additional costs and decreased workplace performance alongside cultural and operational disruptions. The effective prevention of such harmful results requires an active data-based approach that combines predictive analytics to detect potential leave-risk employees so organizations can create specialized and time-sensitive retention initiatives. This paper examines predictive analytics methodology used for employee retention prevention by analyzing employee data through machine learning technology and statistical modeling approaches. The essential goal involves developing predictive frameworks that detect established and new patterns central to employee departure by considering many different data points, including worker statistical information and performance indicators alongside employee survey feedback and past exit statistics. Several evaluation metrics, including precision, recall, F1-score, and AUC-ROC, thoroughly compare the algorithms' performance capabilities. This study evaluates how predictive analytics affects human resource management by showing the importance of converting analytical findings into actionable retention plans. It includes personalized development programs, upgraded pay schedules, and factors that improve the workplace atmosphere. The objective targets organizations with a complete data-driven system to manage proactive workforce retention to increase employee commitment and create a more dedicated and successful workforce.