Chronic kidney disease (CKD) is a major global health concern, and early diagnosis is paramount to avert the disease to worse stages. Historically, many diagnostic tools used were always more invasive, lengthy, and costly than modern diagnostic procedures. Conventional methodologies, however, seem to present some limitations, making it possible to turn to machine learning (ML) methods as a suitable approach to construct accurate and efficient diagnostic models of CKD by analyzing clinical and biochemical characteristics without invasive procedures. In this research, SVM, decision trees, random forest, k-NN, and neural networks are adopted as algorithms for modeling the probability of developing kidney disease. The study also incorporates feature selection and data preprocessing methods on the publicly available CKD datasets to improve the models’ performance. Accuracy, precision, recall, the F1 score, and the area under the ROC curve are employed to assess and compare their performance. The findings shown here prove that using different machine learning algorithms with an emphasis on ensemble methods can achieve the high predictive accuracy of models. Therefore, it can be of help to healthcare professionals to support them in the early diagnosis of CKD. The study emphasizes the viability of applying machine learning to medical diagnosis and calls for its implementation to allow for the early identification of critical conditions and enhance patient care.