Customer churn—the loss of existing clients—poses a major challenge for business growth. This study predicts churn using structured datasets containing demographics, transaction history, and engagement metrics. Multiple machine learning models, including XGBoost, Random Forest, and LightGBM, were trained and evaluated using accuracy, precision, recall, and F1-score. XGBoost achieved the highest predictive performance, effectively identifying at-risk customers while minimizing false positives. The research provides a practical framework for integrating machine learning into customer relationship management systems, enabling timely interventions and data-driven strategies to improve retention, reduce churn, and enhance long-term revenue stability
