chronic kidney disease (CKD) is a long-term condition characterized by the gradual loss of kidney function over time. The kidneys are essential organs that filter waste and extra fluid from the blood so that it can be expelled as urine. Early detection is crucial for managing progression and associated complications. However, manual detection of kidney disease is time-consuming and challenging task in the current setup. In this work, a novel deep learning-based CKD-DBN model has been proposed for identifying CKD in its early stages. Initially, the CT images are gathered from the publicly available dataset and these gathered images are pre-processed using median filter to eliminate noise while preserving edges in the image. The pre-processed images are fed into the GhostNet for feature extraction that extracting significant features from the input images. Finally Deep Belief Network (DBN) classifies the images into Normal or CKD based on these features. The proposed CKD-DBN model is evaluated based on its f1 score, recall, specificity, precision and accuracy. The classification accuracy of 98.82% for the proposed are highly reliable for public available dataset. The proposed CKD-DBN model achieves the overall accuracy by 1.84%, 0.82% and 2.85% comparing to the existing method such as ALO, DNN and RNN