CKD remains one of the significant burdens in world health, usually progressing asymptomatic until the terminal stages. Its early detection and staging are necessary to retard the progression of the disease and improve patient outcomes. This work proposes a real-time and explainable machine learning-based application for CKD stage prediction using clinical and biochemical parameters. Using the publicly available UCI CKD dataset consisting of 400 records and 24 attributes, we develop a robust machine learning pipeline with data preprocessing, feature selection, model evaluation, and model deployment. Data preprocessing includes imputation for missing values, normalization, encoding, and handling outliers, followed by hybrid feature selection using correlation analysis, mutual information, and RFE. Several supervised algorithms were evaluated: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Gradient Boosting. The best performance-a hybrid ensemble model (RF + SVM + GBC via soft voting)-showed an accuracy of 97.8%, precision of 97.6%, and an F1-score of 0.975. Model explainability through SHAP provides insights into both global and per-patient analyses. System deployment is performed using Flask and Streamlit, providing sub-0.5 s response times, thus enabling clinicians to interactively explore real-time model predictions with SHAP visualizations. Unlike previous CKD-related works, this study incorporates explainability and real-time deployment. The results obtained provide evidence for the feasibility of integrating machine learning-driven interpretable analytics into clinical decision-making tools for the management of renal diseases.