Chronic kidney disease (CKD) is found as one of the major global health crises characterized by high morbidity, mortality, and severe economic costs due primarily to late diagnosis and progression to End Stage Renal Disease (ESRD). The primary objective of this endeavor is to integrate the existing research in the realm of machine learning (ML) from the year 2020 to 2025 and develop an architectural framework for a system that is capable of making an accurate and interpretable prediction of the progression of CKD. The analysis of the methodology used in the research work has established the effectiveness of the latest ensemble approaches (with a maximum accuracy of 97.5%) and boosting algorithms, with the finetuned CatBoost model, using Simulated Annealing for feature selection and Cuckoo Search for outlier correction, producing an outstanding Area Under the Curve (AUC) of 0.9993. However, the primary research gap identified across the literature is the translational hurdle: high performing models lack external validation and generalizability across diverse, multi center, real world Electronic Health Records (EHR) data, compounded by the "black box" nature that creates a deficit in clinician trust. To address this gap, the project’s core objective is the Development and Comparative Translational Validation of an Interpretable CKD Prognostic System that moves beyond simple binary diagnosis to predict the temporal progression of CKD to severe stages. One of the most important findings from the synthesis of literature confirms that the synergistic combination of optimization with advanced data handling is essential for maximizing performance; in addition, privacy-preserving architectures such as Federated Learning (FL) have a comparable predictive performance (pooled AUC values of 0.81-0.82) to centralized models. The successful development and validation of this Clinical Decision Support System (CDSS) prototype have significant implications for public health and personalized medicine, as it will facilitate timely intervention, optimized resource allocation, and a substantial reduction in the economic burden of ESRD by driving the adoption of trustworthy AI assisted diagnostic support in primary healthcare settings.
