Over 10% of the world's population now suffers from chronic kidney disease (CKD), and millions die yearly. To extend the lives of those suffering and lower the cost of therapy, CKD should be detected early. Building such a multimedia-driven model is necessary to detect the illness effectively and accurately before it worsens the situation. It is challenging for doctors to identify the various conditions connected to CKD early to prevent the condition. For CKD early detection and prediction, this study introduces a novel hybrid deep learning network model (HDLNet).