The rise in the number of patients with bone fractures in health facilities has raised the need for better and faster diagnostic tools. This systematic review summarizes and evaluates the current literature on applying deep learning technologies for diagnosing bone fractures with more than 40 articles published between 2018 and 2024. The review reveals several key findings: First, Convolutional Neural Networks (CNNs) have reported diagnostic accuracy of between 85% and 99% depending on the type of fracture and the architecture used; ResNet and DenseNet for example have shown better potential. Second, transfer learning approaches have been particularly useful in the task of limited medical datasets, a problem many studies face. Third, the progress in preprocessing methods and data augmentation algorithms has enhanced the model’s performance regardless of different imaging conditions. The methodology analysis shows that successful implementations typically involve multiple stages: methods for medical image enhancement and standardization, feature extraction employing specific neural network architectures, and classification employing conventional as well as deep learning techniques. The review also looks at other measures of performance apart from accuracy measures, such as AUC-ROC curves, precision-recall analysis, and clinical measures of the detection of fractures. However, the study identified the following challenges: Notably, despite the promising results of applying deep learning to help radiologists diagnose fracture, some challenges exist. These include the availability of large annotated datasets, the issue of model interpretability in a clinical environment, and the practical implementation of the models in the healthcare setting. In the final section of the review, the authors discuss potential future research directions and guidelines for closing the translation gap between technology advancement and clinical use in the field of speech-language pathology.