Accurate bone fracture detection from X-ray images is essential for clinical diagnosis and emergency care. Manual interpretation by radiologists can be time-consuming and influenced by fatigue and subjective judgment. To overcome these limitations, this work proposes an automated fracture detection system based on deep learning.The framework employs convolutional neural networks with transfer learning to classify X-ray images as fractured or non-fractured. Pre-trained architectures such as ResNet-50, VGG-16, and DenseNet-121 are fine-tuned using a curated musculoskeletal dataset, with image preprocessing and data augmentation applied to improve robustness. Visual explanation methods are also incorporated to enhance prediction interpretability.Performance evaluation using standard metrics shows that the proposed system achieves an accuracy of 94.2%. Among the evaluated models, ResNet-50 offers the best balance between accuracy and computational efficiency, making it suitable for real-world clinical deployment.