Chronic kidney disease (CKD) is characterized by persistent abnormalities in urinary biomarkers or reduced renal function, posing risks not only of progression to end-stage kidney disease but also of accelerated cardiovascular complications and mortality. The use of computer-aided automated diagnostics can assist nephrologists in early detection and accurate classification, which are essential for improving patient outcomes. This study utilized clinical features of CKD to develop and evaluate six base machine learning classifiers (logistic regression, K-nearest neighbors, AdaBoost, decision tree classifier, random forest, and multilayer perceptron) alongside two novel ensemble models (MKR Stacking and MKR Voting) for CKD prediction and classification. The proposed models were trained on five pre-processed CKD datasets using four robust feature selection techniques, including Lasso, Fisher score, Information Gain, and Relief. The models’ performance was assessed using accuracy, precision, recall, F1-Score, error rate, AUC, and computational time. Among the tested algorithms, MKR Stacking achieved the highest accuracy of 99.50%, outperforming Random Forest (98.75%) and MKR Voting (98%). The XAI technique SHAP and model validation on another CKD dataset highlight the superior prediction capabilities of the proposed ensemble methods compared to traditional classification algorithms. The study further advocates for integrating high-performing models into the Internet of Medical Things and Robotic Process Automation frameworks, enabling real-time monitoring, predictive analytics, and efficient CKD diagnosis. Such integration has the potential to transform CKD management, facilitating early interventions and personalized treatment plans through advanced machine-learning applications.