This study investigates the efficacy of various predictive models in anticipating escalation in employee attrition rates, crucial for strategic workforce management. Leveraging advanced analytics techniques, including K-nearest neighbors (KNN), XGBoost, and gradient boosting machines (GBM), the research evaluates their performance across key metrics. Among the models, XGBoost demonstrated superior predictive capabilities, achieving an accuracy of 90%, precision of 88%, recall of 85%, and specificity of 92%. These metrics highlight XGBoost's effectiveness in accurately identifying at-risk employees while minimizing false alarms. Furthermore, both XGBoost and GBM exhibited robust performance in balancing precision and recall, with AUC-PR values of 0.80 and 0.78, respectively. While KNN showed respectable performance with an accuracy of 85% and precision of 82%, it trailed behind XGBoost and GBM in recall and specificity. These findings underscore the importance of selecting the appropriate predictive model tailored to the specific needs of workforce management. Overall, this research underscores the value of advanced predictive analytics techniques in proactively managing employee attrition. By accurately identifying individuals at risk of attrition, organizations can implement targeted retention strategies, optimize resource allocation, and mitigate the adverse impacts of attrition escalation on organizational performance.