In this paper a system model is proposed to ascertain Chronic Kidney Disease (CKD) using Computed To-mography (CT) scan images and blood samples. The proposed system model is tested using a dataset of 400 patients. To annotate the CT/Magnetic Resonance Imaging (MRI) scan images, the edge detection technique is used in this paper. The system model is proposed considering four units such as edge detection, prediction, virtual assistant and book reader. Edge detection is an image processing technique that is used to identify points or edges in a digital image which are scanned images of CT/MRI. K-Nearest Neighbours (KNN) algorithm is used in this model for disease prediction. Further, virtual assistant and book reader is used to assist the doctor. The proposed model in this paper may be useful for doctor as well as patients for CKD detection. The Machine Learning (ML) approach proposed in this paper may save time and costs for diagnostic screening.