Employees are among the most critical assets of any organization, and the ability to predict their likelihood of leaving is essential for maintaining a stable workforce. High turnover rates can incur significant costs, both in terms of lost talent and the resources required for training and replacement. This study utilizes Analytics Employee Attrition dataset to develop and assess various machine learning models, including Decision Tree, Random Forest, Logistic Regression, AdaBoost, XGBoost, SVM, KNN, and Extra Trees, for predicting employee attrition. By accurately identifying employees at risk of leaving, organizations can implement targeted retention strategies, thereby reducing turnover costs and improving employee satisfaction. The results of this study provide valuable insights into the effectiveness of different predictive models in the context of human resource management, aiding companies in their efforts to retain top talent and maintain a competitive edge.