CKD is a condition that highly requires early detection in providing interventions on time; it has a very significant influence on patient care outcomes. The current paper proposes a novel approach to CKD detection and classification by introducing an RNN model tailored to handle complexities within medical data. The models proposed were compared with a large amount of the traditional and advanced machine learning techniques, including Logistic Regression, SVM, Decision Tree, Random Forest, CNN, and DNN. The RNN model really did an incredible job, registering 98.5% of accuracy, precision, recall and F1, exceeding all existing methods. These results prove sufficient potential of the model in accurately and reliably identifying CKD cases, hence making it very effective for use in clinical diagnostics. Advanced deep learning algorithms come with great promise for various applications in medical science to improve the diagnosis and care of patients. Further research works based on complex machine learning models will be used to overcome other critical challenges of healthcare.