In the current competitive job market, industries are confronting significant hurdles with employee attrition and notable turnover rates. To address these pressing concerns, organizations are progressively leveraging artificial intelligence (AI) to forecast employee attrition and deploy efficient retention strategies. Many existing machine learning approaches have tried to predict employee attrition rate across diverse organizations. However, we notice that they have limited ability to deal with fairness and interpretability while predicting employee attrition rate. To address this disparity, we present FIEAP in this paper, an interpretable machine learning method designed for fair prediction of employee attrition. Our approach employs ensemble learning with a stacking strategy, incorporating four distinct machine learning algorithms. To ensure fairness and enhance interpretability, we integrate LIME (Local Interpretable Model-Agnostic Explanations). We evaluate the proposed approach using a real-life dataset from CPC HR Employee Analytic and report on its performance in terms of prediction accuracy, fairness metrics, and feature importance. The results demonstrate the potential of our approach to provide clear explanations while maintaining fairness in attrition prediction.