Chronic Kidney Disease (CKD) is a growing global health problem that is associated with high healthcare costs and a high mortality rate. Earlier diagnosis is very important since CKD is irreversible in nature but its symptoms are difficult to detect until the later stages. Data mining techniques can aid physicians in the timely diagnosis of CKD and change the trajectory of the disease’s progression. In the first phase of the study, five machine learning models - decision tree, random forest, support vector machine, artificial neural network, and a stacked hybrid model- were used to establish CKD diagnostic models. In the second phase of the study, a cluster analysis was carried out to identify trends in the positively diagnosed samples and to observe whether the clusters of samples conform to the six clinical stages of CKD.