Chronic kidney disease (CKD) establishes substantial health risks, potentially progressing to life-threatening stages necessitating dialysis or surgery for survival. Early detection and effective management are crucial in mitigating its progression. This study employs ensemble learning algorithms—AdaBoost, XGBoost, CatBoost, and LightGBM—to enhance diagnostic accuracy and refine patient management strategies for predicting CKD. Utilizing the CKD dataset from the UCI Machine Learning Repository, the models are evaluated through cross-validation, focusing on metrics such as accuracy, sensitivity, and specificity. The combined AdaBoost & CatBoost model emerges as highly effective, achieving precision of 0.99, accuracy of 99.9%, and sensitivity of 1.0. This highlights a synergistic synergy between AdaBoost and CatBoost, underscoring their potential in enhancing predictive capabilities for CKD, thereby offering promising avenues for improving clinical outcomes through early intervention and targeted therapy. © 2025 Elsevier B.V., All rights reserved.