Chronic kidney disease (CKD) is a major issue that has been growing at a constant rate. An individual can only survive for a few days without functioning kidneys, which leads to dialysis or kidney transplantation. In CKD, the kidneys are harmed and cannot cleanse blood as they normally do. Heart conditions, anemia, bone issues, excessive potassium and calcium levels, and anemia are among the frequent consequences of kidney failure. In the worst-case scenario, total renal failure necessitates a kidney transplant for survival. Early identification of CKD can significantly improve quality of life. Machine learning methods like Random Forest, Naïve Bayes, Decision Trees, SVM, and KNN are effective for early CKD identification. The Random Forest model, particularly, has shown excellent performance on the UCI CKD dataset, achieving 100% accuracy. Due to its superior accuracy compared to other models, the Random Forest classifier was employed for CKD prediction.