Tehran University of Medical Sciences Archives of Anesthesiology and Critical Care 2423-5849 0 0 2026 08 01 1627 1627 Dariush Abtahi Clinical Research and Development Unit, Department of Anesthesiology, Imam Hossein Hospital, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 2026 04 29 2026 05 24 Background: The integration of artificial intelligence (AI) and machine learning (ML) technologies in anesthesiology has emerged as a transformative force in perioperative medicine. This narrative review synthesizes current evidence on AI applications across the perioperative continuum. The objective is to provide a comprehensive overview of AI applications in anesthesiology, examining current implementations, clinical outcomes, and future directions across preoperative assessment, intraoperative monitoring, drug delivery, postoperative care, and clinical documentation. Methods: A systematic literature search was performed across several databases for peer-reviewed publications on AI applications in anesthesiology from 2015 to June, 2025. If the papers were peer reviewed and come from quality journals. Sixty-six papers from reputable peer-reviewed medical journals of high quality were selected for analysis. Results: The use of AI-based applications for anesthesiology can be summarized into 5 specific domains: predictive modeling to predict surgical risk assessment, real-time monitoring systems that measure anesthesia depth, automated drug delivery systems (and devices), postoperative complication prediction after surgery, and natural language processing (NLP) to assess clinical documentation. Recent advances show better patient outcomes, clinical decision-making and operational efficiency. Conclusion: AI technology holds substantial potential to enhance anesthesiology practice along the entire perioperative continuum. Although some challenges need to be addressed in respect of clinical integration and validation, we believe that there is sufficient evidence to justify undertaking further development work with a view towards implementation of AI-assisted anesthesia care. https://aacc.tums.ac.ir/index.php/aacc/article/view/1627