There are currently a large number of people all around the world who are suffering with chronic kidney infections. Today, everyone is attempting to be health-conscious, despite the fact that, owing to overwork and a hectic schedule, one only pays attention to one's health when symptoms appear. A few factors, for example, dietary habits, temperature, and expectations for daily luxuries, cause large numbers of people to be afflicted unexpectedly and without knowledge of their condition. Finding a persistent kidney disease is often intrusive, costly, time- consuming, and dangerous. The main reason why many people die without receiving care, especially in many developing countries where resources are few. As a result, early diagnosis and recognition of illness remains important, particularly in non-industrialized countries where illnesses are typically studied in late stages. However, if it does not show any symptoms at all, or if it does not show any disease-specific symptoms, it is very difficult to find &predict the disease type, detect and prevent such a disease, and this could lead to permanent health damage as well as the formation of new diseases, but machine learning can be a hope, as it is the best way for prediction and disease analysis. We will utilize data from CKD patients with 14 variables, as well as several machine learning approaches such as Decision Tree, SVM, and CNN model. To create an efficient machine learning model with the highest accuracy (by comparing several machine learning models) in predicting whether or not a person has CKD and, if so, how severe it is. © 2024 Elsevier B.V., All rights reserved.