Employee attrition continues to challenge enterprise organizations striving to maintain productivity, institutional knowledge, and workforce stability. This study introduces a comprehensive machine learning framework architected within SAP SuccessFactors to predict employee turnover characterized by high predictive accuracy and model interpretability. The framework leverages integrated workforce datasets encompassing performance metrics, compensation evolution, engagement trends, promotion history, and career mobility to identify early signals of attrition risk. A structured data pipeline incorporating feature selection, class rebalancing through SMOTE, and ensemble model comparison was developed to ensure robustness and fairness in prediction. Among the evaluated models, Random Forest demonstrated superior performance with an accuracy of 86 percent and balanced precision-recall metrics, supported by SHAP-based interpretability for transparent feature attribution. Beyond its predictive capability, the framework emphasizes system-level integration, allowing HR teams to embed attrition forecasts directly within SAP SuccessFactors dashboards for real-time decision support. The study contributes a replicable architecture that unites predictive analytics, ethical governance, and organizational intelligence to transform HR operations from reactive retention efforts to proactive, data-driven workforce planning. Its implications extend to academic research in HR analytics and practical deployment within enterprise talent ecosystems, establishing a foundation for intelligent and sustainable workforce management.