Renal disease affects between eight and sixteen percent of global populations while increasing complications from diabetes and heart disease and causing major death rates along with illness progression. Early detection together with appropriate interventions for treating chronic kidney disease depends on conventional diagnostic approaches whose specificity and sensitivity often remain inadequate. This research demonstrates how statistical technologies enable early identification of chronic kidney diseases through the analysis of their potential benefits. A comprehensive dataset of clinical trial data and public resources was used to create validated predictive models through advanced algorithms including decision tree and K-nearest neighbors as well as random forest and support vector machine. These findings show that the predictive model is valid (99.00%) which confirms the effectiveness of ML techniques in identifying high-risk patients. The study aims at being incorporated into clinical practice through machine learning, which could transform nephrology in favor of patient outcomes, eliminate questions of data privacy and address algorithmic bias. The research findings demonstrate why early intervention in chronic kidney disease therapy remains essential while creating new possibilities for case evaluation techniques.