Employee attrition poses a persistent and costly challenge for modern organizations, with replacement expenses ranging from 33% to 200% of annual salaries and aggregate annual losses approaching $1 trillion globally. Machine learning models have demonstrated considerable potential for predicting employee turnover; however, these systems frequently exhibit systematic biases against demographic groups including gender, age, and departmental representation, undermining both fairness and organizational trust. This research presents a comprehensive fairness-aware explainable AI framework that integrates bias mitigation techniques with predictive modeling for employee attrition forecasting. The proposed architecture comprises three synergistic components: a Random Forest classifier incorporating fairness constraints achieving 94.2% prediction accuracy while reducing demographic parity disparity by 72%, an explainability module leveraging SHAP values to provide transparent justifications for each attrition risk prediction, and a strategic decision support system generating prioritized intervention recommendations. Experimental validation on a 500,000-record dataset containing 35 employee attributes demonstrates that the framework successfully identifies high-risk employees while maintaining equitable treatment across demographic groups. The system delivers interpretable risk factors enabling HR practitioners to understand the rationale behind employee risk classifications and formulate targeted retention strategies.Novelty in the Research This research introduces several novel contributions that distinguish it from existing workforce analytics solutions. First, unlike commercial platforms that treat fairness as an afterthought, our framework integrates demographic parity and equal opportunity constraints directly into the model training and post-processing pipeline, achieving a 72% reduction in fairness disparities while maintaining high.