The integration of Artificial Intelligence (AI) and Machine Learning (ML) is fundamentally transforming diagnostic and therapeutic paradigms in orthopedic and orthodontic practice. This review delineates the methodological frameworks, clinical applications, and translational integration of AI/ML technologies, emphasizing their role in enhancing precision, efficiency, and patient-centered care. It aims to provide a comprehensive analysis of how these tools are applied from image interpretation to outcome prediction and workflow management. Key methodological frameworks include convolutional neural networks (CNNs) for automated analysis of radiographs, CT, and CBCT scans, and ensemble learning models for prognostic risk stratification. Major applications encompass AI-assisted fracture detection, spinal deformity analysis, automated cephalometric landmark identification, and prediction of orthodontic treatment duration and stability. Furthermore, AI-driven clinical decision-support systems optimize surgical planning, resource allocation, and postoperative monitoring. Concluding remarks underscore the significant potential of AI/ML to augment clinical decision-making and personalize treatment pathways. However, successful translation requires rigorous validation, seamless workflow integration, and addressing ethical challenges related to data bias and algorithmic transparency. Future research must focus on developing robust, generalizable models and establishing frameworks for their responsible implementation in interdisciplinary clinical settings.