The modern organizations are grappling with some very crucial challenges in anticipating the attrition of employees because they depend on the intuitively-based assessment method and survey-dependent methodology that cannot be predicted and interpreted. The paper presents a framework of explainable ensemble machine learning, which combines XGBoost and SHAP (SHapley Additive exPlanations) analysis to predict workforce turnover with the help of historical human resource, productivity, and engagement indicators. The methodology will include a comprehensive data preprocessing, feature engineering, construction of the ensemble model, and validation based on interpretability, which will be conducted in the form of ROC-AUC analysis, confusion matrices, and decomposition of SHAP values. Results of an experiment prove the 83 percent classification accuracy and competitive AUC performance (0.67) and the SHAP analysis shows that the EnvironmentSatisfaction, YearsWithCurrManager, and JobSatisfaction are the key predictors of the attrition. The framework has transparency-accuracy synergy, where human resource practitioners use evidence-based retention strategies to address high-risk groups of employees. It is an approach that helps to develop organizational decision-making, reduce recruitment costs, and improve the strategic workforce planning with the help of quantitative behavioral analytics in dynamic enterprise settings by converting opaque algorithmic-based predictions into workforce intelligence that can be acted upon.