This paper aims to build a web application for bone fracture classification using deep learning techniques. The application was created using TensorFlow and the Keras library. Architecture: The architecture is made up of convolutions for feature extraction, max-pooling for the reduction of dimensions, and dense layers for classification. The model showed extraordinary accuracy for the severity of kinds of bone fractures. That is quite in order for practical applications. One web application based on the Flask framework was built to let user interaction. The application allows users to input medical images of their choice in various formats, and based on the trained CNN model, it predicts the image class by showing the results of prediction. In this study, deep learning techniques were successfully integrated into an actual web application and proved to be such a worthy tool for preliminary bone fracture analysis; it shows the capability of artificial intelligence in improving the healthcare diagnostic process. The model performed well in various classes of fractures; for example, in high fractures, the accuracy rate was 96%, 90% for low fractures, 94% for moderate fractures, and 92% for not fractured cases. The precision, recall, and F1-scores ranged from 0.91 to 0.96 with highly consistent values in high values, making this model effective for the right diagnosis of different fractures. Probably, real-time processing, inclusion of 3D imaging modalities like CT scans, and alternative better methods of pretreatment to handle noisy images can be included in future works and developments within the system proposed. Added accuracy, usability, and confidence of providers about the system will be brought with explainable AI and an enlarged dataset for better generalization.