Researchers at the Queensland University of Technology have demonstrated that artificial intelligence can accurately predict workplace musculoskeletal injury risk by analyzing complex, multifactorial data across nine specific body regions. The study, led by Ph.D. researcher Mehrdad Hassani from the School of Public Health and Social Work, challenges traditional ergonomic models that primarily attribute office-related injuries to poor posture and prolonged sitting. By processing anonymized data from 810 office workers across four public datasets, the research team trained six distinct machine learning models to evaluate how physical, psychosocial, and organizational variables interact. The results revealed that injury risk is highly localized, with distinct predictive factors varying significantly by anatomical region. Conventional linear assessment tools consistently underestimated these interactions, whereas the AI-driven approach identified nuanced, non-linear risk patterns. A primary finding of the research is the substantial influence of sleep duration and psychosocial stressors on neck, lower back, and hip discomfort. Poor sleep quality emerged as a top-tier predictor, supporting emerging clinical evidence that inadequate rest impairs tissue repair and heightens pain sensitivity. Organizational dynamics, including heavy workloads, low job autonomy, and insufficient workplace support, were also strongly correlated with cervical and lumbar strain. These insights indicate that musculoskeletal disorders are not merely biomechanical issues but are deeply intertwined with mental well-being and workplace structure. Anthropometric data further refined the predictive accuracy. Worker height and body mass index proved critical for estimating injury probability in the wrists, knees, and upper back, underscoring the necessity for highly adjustable workstations and sit-stand configurations. Conversely, emotional demands and perceived work meaning showed moderate predictive value specifically for shoulder and upper back pathology. Age, weight, and professional tenure completed the core set of influential variables. The study establishes a practical framework for deploying machine learning in occupational health and safety. Rather than applying generalized ergonomic guidelines, the findings advocate for targeted, body-specific intervention strategies. By mapping risk profiles to precise anatomical zones, employers and health professionals can allocate resources more effectively, designing tailored workplace adjustments that address both physical setups and organizational stressors. This research marks a significant advancement in predictive occupational medicine, shifting the industry away from one-size-fits-all safety protocols toward data-driven, individualized risk management. As AI modeling becomes more accessible in corporate wellness programs, organizations will be better equipped to preempt injuries before they manifest, reducing long-term absenteeism and improving overall workforce resilience.