Chronic Kidney Disease (CKD) detection and classification have been considered the most valuable measurements for early medical intervention and improvement of outcomes. This integrated model proposes using an autoencoder with CNN, introducing higher accuracy and efficiency into CKD diagnosis. This is the AE + CNN model, which negates some of the disadvantages mentioned about the traditional machine learning methods. An autoencoder for feature extraction and reduction of dimension and CNN to capture higher-order information due to complex patterns of data will perform better in classification. Compared to the performance of decision trees, SVM, random forest, and a solo CNN, the proposed model shows much better accuracy, sensitivity, specificity, and F1-score tested with the CKD dataset from Kaggle. Besides this, competitive time efficiency makes an AE + CNN apt for applications that involve real-time diagnosis. These findings from the study also underline the opportunity for further, new deep learning methods to accelerate early detection of CKD and, therefore, serve as a strong tool in enhancing medical diagnostics and care for patients.