Background: The surgical management of the facial skeleton has been reshaped over the past two decades by a sequence of digital technologies that now extend across the entire care pathway, from image acquisition and diagnosis to planning, intraoperative execution, and follow-up. Artificial intelligence (AI) has entered this landscape more recently, and its precise contribution to facial bone surgery, as distinct from the broader fields of craniomaxillofacial and aesthetic surgery, remains incompletely defined. Methods: We performed a narrative review of the English-language literature, prioritizing studies published in peer-reviewed journals indexed in major databases, that examined the application of AI and allied digital technologies, including automated segmentation, virtual surgical planning, computer-aided design and additive manufacturing, intraoperative navigation and augmented reality, and piezoelectric osteotomy, to facial bone surgery. Results: Automated deep-learning segmentation of craniofacial computed tomography now approaches or exceeds the accuracy of manual tracing while reducing processing time by an order of magnitude. Virtual surgical planning has become the standard of care in orthognathic and reconstructive procedures, with systematic evidence of accuracy equal to or better than conventional model surgery. Machine-learning models predict postoperative facial soft-tissue change and, in early studies, propose surgical plans directly from cephalometric data. Patient-specific implants fabricated by additive manufacturing have demonstrated high success rates, with pooled success approaching 95%, particularly in orbital and mandibular reconstruction. Intraoperative navigation and augmented reality improve precision in anatomically complex regions, and piezoelectric osteotomy reduces collateral soft-tissue injury. Conclusions: AI and digital technologies have moved from adjuncts to integral components of facial bone surgery. They improve precision, efficiency, and reproducibility, yet high-level comparative evidence remains limited, and questions of generalizability, regulation, and cost persist. Progress depends on prospective multicenter validation and on the development of interoperable, transparent, and clinically integrated systems.