This study evaluates the effectiveness of machine learning techniques in predicting healthcare employee attrition using the Healthcare Attrition Dataset. The research addresses employee turnover challenges in healthcare organizations that impact operational costs and productivity. The methodology incorporates data preprocessing, including missing value treatment and categorical variable encoding, followed by the implementation of eight machine learning algorithms such as Gradient Boosting, AdaBoost, CatBoost etc. Models were trained on both raw and transformed data using Min-Max Scaling, Standardization, L1 Normalization, and L2 Normalization. The models’ predictive capabilities were assessed through comprehensive performance metrics encompassing classification accuracy, precision-recall trade-offs, and receiver operating characteristic analysis. Results demonstrate AdaBoost’s superior performance, particularly with scaled data, indicating its potential for healthcare attrition prediction. The study highlights data preprocessing’s significance in machine learning and establishes a framework for future healthcare attrition analytics research.