A hybrid machine learning framework designed to predict burnout levels and employee retention among IT professionals in urban India a sector increasingly challenged by mental health issues, high attrition, and work-life imbalance. The proposed framework integrates Gradient Boosting Regression for continuous burnout score prediction and Random Forest Classification for binary retention outcomes. Data were collected from 300 IT employees across five metropolitan firms, encompassing cross-sectional and temporal variables related to work conditions, wellness policies, and demographic profiles. Rigorous preprocessing, feature encoding, and 5-fold cross-validation ensured robust model performance. The burnout prediction model achieved an R2 score of 0.87 and a Mean Absolute Error (MAE) of 4.12, while the retention classifier yielded an accuracy of 85%, F1-score of 0.82, and AUC-ROC of 0.88. Key predictors included flexible hours, remote work options, and job satisfaction, with gender emerging as a significant moderating factor—female employees demonstrated 12% greater sensitivity to burnout-related outcomes. The findings highlight the utility of predictive analytics in identifying high-risk employees and guiding personalized wellness and retention strategies. This hybrid framework offers HR leaders a scalable, data-driven tool to optimize workforce well-being and reduce attrition in the evolving landscape of Indian IT workplaces.