One of the leading causes of illness and mortality in the world's medical communities is chronic kidney disease (CKD). Patients often misdiagnose CKD since there are no symptoms in the early stages of the illness. Individuals living with HIV are more likely to develop critical care kidney disease (CKD). Early diagnosis of CKD prevents the illness from worsening and enables patients to get treatment more quickly. The application of machine learning algorithms for illness categorization and prediction in healthcare has increased due to the availability of pathology data. The categorization of CKD using machine learning models is presented in this research. For individuals with CKD, the CKD stages are also determined based on the glomerular filtration rate. The DNN model performs better, diagnosing CKD patients with HIV with 99% accuracy.