The study focuses on renal problems, which have emerged as a significant public health concern, and the significance of prompt management and better patient outcomes. The study suggests using machine learning techniques to anticipate kidney diseases in order to address this problem. The study's dataset includes a variety of clinical, demographic, and laboratory data from different patient populations. Utilizing feature engineering and preprocessing procedures, the input data's quality and relevance are increased. A group of machine learning models, including Random Forest, Gradient Boosting, and Support Vector Machines, are subsequently developed. Utilizing each particular technique's benefits while minimizing its drawbacks is the aim. The findings show that the ANFIS model achieves a precision of 100% compared to the NB-CbH model's 90.2%. This suggests favorable outcomes in the early detection of chronic kidney disease (CKD). The suggested model may help nephrologists make wise choices when necessary, potentially enhancing patient quality of life on a global scale. To further increase the predictor model's accuracy and classification rate, the study contends that deep learning techniques can be used in the future. The incorporation of deep learning into this predictor model should result in even more precise and reliable predictions for renal illnesses. Deep learning has demonstrated tremendous potential in a variety of medical applications.