Predictive analytics, powered by machine learning (ML) and deep learning (DL), is reshaping clinical decision-making across musculoskeletal medicine and orthodontics. This review examines next-generation AI-driven predictive systems, their applications in orthopaedic risk stratification, fracture prediction, and orthodontic treatment planning, alongside a comparative analysis of enabling technologies and real-world performance benchmarks. A four-layer Predictive Healthcare Framework is proposed to guide systematic deployment. Pooled evidence from 38 studies demonstrates predictive accuracy ranging from 87% to 96% for key clinical endpoints, reduction in diagnostic turnaround of up to 52%, and improvement in patient-reported outcomes (PROs) of 34% relative to conventional care. Implementation barriers including data interoperability, algorithmic bias, and regulatory compliance are critically examined, and a roadmap for responsible clinical integration is presented.