This systematic review explores the integration of artificial intelligence (AI) and machine learning (ML) in managing shoulder musculoskeletal disorders. Conducted by Umile Giuseppe Longo and Kristian Samuelsson, the review includes 33 studies that apply AI models to shoulder-related clinical data. The studies were meticulously screened, and the risk of bias was assessed using validated tools. AI demonstrated robust accuracy in diagnosing rotator cuff tears and other shoulder injuries through imaging, with AUC scores ranging from 0.81 to 0.94. Predictive algorithms showed moderate to strong efficacy in forecasting post-surgical outcomes, such as patient-reported outcomes and retear rates. AI also accurately identified shoulder implant types from radiographic images, achieving up to 97% accuracy. Despite these promising results, the review highlights the need for standardization and validation due to methodological variability across studies. The findings suggest AI's potential to enhance diagnostics, treatment planning, and surgical outcomes in shoulder orthopaedics, although further research is needed to generalize these results to broader populations.