Machine learning evaluation has found its way into the world’s most important fields, such as medicine and healthcare, which is a crucial shift in our way of thinking about solving the complex clinical issues. This development is necessary to the healthcare industry, as medical information is highly complicated in its structure and the analysis of such data is challenging by its essence. The approach to medical research can make a great difference in the field of medical study, and the way in which kidney disease is identified is one of the most important areas. The kidney-related diseases that present a challenge in terms of diagnosis include Chronic Kidney Disease (CKD), Acute Kidney Injury (AKI), and UTI-related renal dysfunctions, which have complex clinical behaviors and data heterogeneity. Ensemble learning, which involves the combination of several classifiers, has demonstrated high accuracy and strength compared to conventional single models in the prediction of these diseases. This is a review of 30+ recent studies (2019-2025) on bagging, boosting, stacking, and voting ensemble methods on kidney diseases. Overall, bagging has been shown to be common in CKD prediction with notable accuracy, boosting demonstrated effective in heterogeneous AKI datasets, and stacking ensembles, though underutilized, exhibit the highest accuracy and generalizability across datasets. The review discusses methodological trends, comparative results, and future research paths to optimize ensemble models for renal disease diagnostics and prognosis.