Diabetes is a serious global health issue and early detection is important to avoid serious complications and ensure positive results for the patient. Currently, the standard process of diagnosing the condition is the use of expensive and timeconsuming invasive blood tests. However, this study aims to provide a new method of automatically classifying the stage of diabetic condition by utilizing the visual properties of the patient’s tongue. The proposed method involves processing the images of the tongues by identifying the vital visual properties such as the color, coatings, and texture of the tongues that are correlated to the health of the metabolism of a patient. A combination model of a convolution neural network and a radial basis function neural network is designed to classify the stage of the diabetic condition of a patient. This model is trained on an expanded pool of 6,000 images that are initially obtained from 200 images of the tongues of patients with three levels of conditions (healthy, pre-diabetes, and diabetic). Experimental findings clearly establish the fact that the hybrid model has been able to produce a 95% level of overall accuracy with high precision, recall, and F1-score for all classes. Based on the trained model, an users-friendly web interface is designed to make predictions and offer personalized recommendations for lifestyle changes. Experimental findings clearly establish the fact that tongue image analysis can be a simple effective means for diagnosing diabetes, ranging from a noninvasive approach to the conventional ones. This approach can easily help make a significant contribution to the management as well as prevention of diabetes.