Chronic diseases, including chronic kidney disease (CKD), are major health care problems because they are not symptomatic at initial stages, and they are expensive to treat once they reach the late stage. This paper presents a diagnostic pipeline powered by AI that aims at the early identification of CKD based on real-life Electronic Health Records (EHRs). Offering powerful preprocessing capabilities and combining with the latest machine learning algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM), the framework efficiently handles the class imbalance and missing data. Multiple stratified cross-validation (5-fold), feature importance assessment, and various other experiments show excellent predictive performance (up to 99.17%) and high diagnostic agreement among models. The most important characteristics in the early detection of CKD were hemoglobin, serum creatinine, and red blood cell count. The findings show the possibility of AI-based approaches to enhance clinical decisions and enable prompt treatment of chronic conditions.