Chronic Kidney Disease i.e. CKD, is one of the biggest issues in health worldwide. It occurs due to a gradual decline in renal functions accompanied by a high level of morbidity and mortality. This paper discusses the multifaceted nature of the etiology, the clinical manifestations, and the diagnostic approaches related to CKD, while highlighting the emerging role of ML in improving disease prediction and management. It proposes an ensemble learning approach as an aggregate of several ML algorithms to enhance accuracy and reliability of CKD prediction. In the proposed ensemble approach SVM, logistic regression, random forest and decision tree algorithms with voting-based learning have been used. This model has been tested using ten-fold cross validation technique on the input dataset. Experimental results indicate that the accuracy of the prediction of CKD with proposed ensemble method is 90.35% against individual algorithms. This method, therefore, has great scope for early detection, risk stratification, and personalized treatment planning in the management of CKD.