Tehran University of Medical Sciences Journal of Craniomaxillofacial Research 2345-5489 13 3 2026 10 21 279 288 Mitra Montazerlotf Department of Information Technology Management, Faculty of Management and Economics, Sciences and Research Branch, Islamic Azad University, Tehran, Iran. Nima Dehghani Department of Oral and Maxillofacial Surgery, School of Dentistry, Tehran University of Medical Sciences, Tehran, Iran. 2026 05 14 2026 08 30 Introduction: Rhinoplasty remains one of the most technically demanding procedures in facial plastic surgery. Artificial intelligence (AI) has emerged as a transformative technology with po-tential to address longstanding challenges in surgical subjectivity, outcome prediction, and patient communication. This narrative review synthesizes current evidence on AI applications across all phases of rhinoplasty care—from preoperative simulation and patient counseling to in-traoperative guidance and postoperative outcome assessment—while critically exam-ining persistent limitations and ethical considerations.  Materials and Methods: A literature search was conducted across PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar for studies published between 2015 and 2026. Thirty-nine studies met the inclusion criteria. Results: AI applications span five principal domains: preoperative planning and simulation; aes-thetic analysis and attractiveness quantification; large language model–based patient counseling; intraoperative guidance and augmented reality; and postoperative outcome evaluation. Reported model accuracies ranged from 82% to 97%. Large language mod-els performed comparably or superiorly to expert surgeons in structured counseling eval-uations.  Conclusion: AI offers substantial promise for enhancing precision and reproducibility in rhinoplasty, but algorithmic bias, limited dataset diversity, absence of prospective validation, and unresolved ethical concerns currently restrict widespread clinical adoption. AI should be regarded as a powerful adjunct to, rather than a replacement for, surgical expertise.  Keywords: Artificial intelligence; Rhinoplasty; Machine learning; Deep learning; Large language model; Surgical simulation; Outcome prediction; Facial plastic surgery.  https://jcr.tums.ac.ir/index.php/jcr/article/view/652 https://jcr.tums.ac.ir/index.php/jcr/article/download/652/508