A major worldwide health problem, chronic kidney disease (CKD), needs efficient early detection techniques. This study uses clinical data to investigate novel machine learning techniques for CKD prediction. Advanced data preparation methods including collaborative filtering for missing values and strategic attribute selection are part of our approach. The Extra Trees Classifier and XGBoost stood up as the most accurate and least biased of the six machine learning algorithms that were assessed. To manage missing or inconsistent data, advanced preprocessing methods like feature selection and imputation were utilized. These were thoroughly assessed using a range of criteria, with a focus on their interpretability and usefulness in actual clinical settings. The results demonstrated the XGBoost model’s potential for clinical integration by showing that it performed better in terms of prediction. This research study shows that machine learning can revolutionize the forecast of chronic kidney disease (CKD), causing better health results over time. The outcomes show that machine learning can make early CKD detection better, meaning medical treatment can start on time, and health results are improved. Moreover, the paper refers to the practical aspects of collecting data and the importance of using clinical data in predictive models.