Chronic Kidney Disease (CKD) is characterized by impaired kidney function and its ability to maintain overall health. It often progresses slowly over time and can lead to serious complications if left untreated. In order to prevent consequences such as hypertension, anemia, bone fragility, poor nutrition, and neurological dysfunction, early identification of chronic kidney disease (CKD) is imperative. Large-scale datasets are mined for insights in the healthcare industry that help with well-informed decision-making. Machine learning has applications in user authentication, fraud detection, and medical science, demonstrating its adaptability to a range of problems, including the management of chronic kidney disease. Numerous machine learning algorithms and data mining classification techniques are used in the context of forecasting chronic diseases like CKD. The purpose of this study is to create a novel decision-support system for forecasting chronic kidney disease. In this study, the execution times, accuracy, and precision of Random Forest and SVM-KNN fusion are compared. The results indicate that SVM-KNN performs better in prediction accuracy than Random Forest, providing important insights into machine learning methods for CKD prediction.