Chronic kidney disease (CKD) is when the kidneys are damaged and cannot correctly filter waste and excess fluids from the blood. About 37 million people in the United States have Chronic Kidney Disease (CKD). Early detection and diagnosis of CKD are essential to preventing its progression to kidney failure. Machine learning models can assist in predicting CKD. This project will use a decision tree to analyze National Center for Health Statistics (NCHS) data. Variables such as age, gender, medical history, and laboratory test results will be used. By identifying patterns in the data, models can predict a patient’s risk of developing CKD, allowing for early intervention and management.