Rapid advancements in technology have significantly influenced various domains, particularly the medical field, where innovative approaches are enhancing diagnostic and therapeutic procedures. Despite these developments, conventional techniques such as X-ray imaging remain indispensable due to their reliability, cost-effectiveness, and widespread availability. X-ray imaging is extensively utilized for the detection of bone fractures; however, certain fractures may be subtle or situated in anatomically complex regions, thereby increasing the likelihood of misdiagnosis. To address these challenges, this study proposes an intelligent automated system for the detection and classification of bone fractures using advanced computational methods. The proposed framework consists of two primary stages: initially, X-ray images are subjected to preprocessing techniques, including noise reduction, contrast enhancement, edge detection, and segmentation, to improve image quality and extract relevant structural features; subsequently, a backpropagation neural network is employed for classification, wherein the model is trained on processed images to learn discriminative features of various fracture types and evaluated on unseen data to assess its performance. Experimental results demonstrate that the proposed system achieves high levels of accuracy and efficiency, indicating that the integration of image processing techniques with neural network-based classification can significantly improve fracture detection and assist healthcare professionals in making more precise and timely clinical decisions.