A non-trivial effort made in the direction of advancement in machine learning (ML) through the past decade has brought us much ahead in clinical and research settings. Chronic diseases are finding increased occurrence and their negative effects have caused serious worry on a worldwide scale. This research explores traditional ML algorithms for improving of diagnosis and prognosis of chronic kidney disease (CKD). We study decision trees, support vector machines, and Naive Bayes to arrive at an accurate, robust, and explainable model for predicting the progression of CKD. The comparative study invokes analysis of the proposed models in uncovering patterns, extracting vital components from the diverse patient information and medical imaging to generate accurate prognostic insights. The study not only assists in the early identification of CKD but also simplifies the process of treatment strategy. The proposed solution attempts to bring about enhancement in patient care and the allocation of resources within healthcare systems. As technology progresses, the integration of such intelligent systems in clinical settings has the potential to bring about remarkable alterations to the prognosis of other chronic medical conditions in addition to CKD benefiting not only the patient community but also the medical fraternity.