In rural areas, Distal Radius Fractures (DRF) remain neglected leading to deformed and painful wrists mainly because of the non-availability of Orthopaedicians. Our objective was to develop an artificial intelligence-based web tool which can assist in diagnosis and guide the treatment of distal radius fractures. A Deep Convoluted Neural Network (DCNN) model was developed for the diagnosis of DRF. The goal of this work was to develop an artificial intelligence system that can learn to utilize X-ray pictures to correctly diagnose distal radius fractures with a small amount of information. Labelling assessments with fractures and overlaying fracture masks generated images that may be used for testing and training segmentation and classification methods. The DCNN model analyzed DRF based on three views: lateral, anteroposterior, and combined lateral and anteroposterior views. The experimental outcomes demonstrate that the recommended model increases the classification accuracy rate of 99.3%, sensitivity rate of 96.5%, specificity rate of 97.8%, and F1-score rate of 95.6% and reduces the error rate of 11.2% compared to other popular approaches.