Bone fracture identification is one of the critical aspects in medical diagnosis since early and accurate detection greatly impacts the outcome for patients. Thus, the review discusses the improvement of detection strategies for bone fractures to shift from classical image-processing-based methods toward those reliant on machine learning and deep learning. Traditional methods, which depend on handcrafted feature extraction and image enhancement techniques, can be used in the detection of fractures; these are usually bound by the requirement for expert-designed parameters. Machine learning allows for building automated classification models with an improved level of accuracy of detection, courtesy of data-driven learning-but still, manual engineering. In recent years, deep learning has revolutionized the field by harnessing neural networks, especially convolutional neural networks (CNNs), which have the capability of automatic extraction of hierarchical features from medical images. This article provides a comprehensive analysis of the benefits and limitations of such approaches, including their performance metrics, computational requirements, and real-world applicability in clinical settings. It finally identifies large research deficiencies and proposes prospective avenues for developing more resilient and interpretable fracture detection systems.