Artificial intelligence (AI) is transforming athletic training, offering novel tools to enhance performance, optimize recovery, and reduce injury risk. This systematic review synthesizes findings from 55 studies evaluating the integration of AI—particularly machine learning (ML) and deep learning (DL)—into sport-specific monitoring systems. These systems incorporate wearable data, video analysis, force measurements, and psychological profiling to support real-time decision-making in training and rehabilitation. Core applications are categorized across two main domains: (1) injury prevention, including movement analysis, fatigue/stimulus monitoring, and recovery forecasting; and (2) advancements to sport, including performance prediction, enhancement, and sport-specific adaptations. Across studies, models such as support vector machines (SVM), convolutional neural networks (CNN), long short-term memory (LSTM), and ensemble methods demonstrated predictive accuracies exceeding 90% in many contexts. This review highlights the potential of AI to augment the precision and scalability of athletic training, while also identifying current limitations, including data standardization, interpretability, and ethical oversight. Recommendations are offered to advance the responsible implementation of AI in athletic training for sport.