Machine Learning and Postoperative Pain: A 2026 Critical Narrative Review

Postoperative pain remains a common and clinically important problem, with inadequate control contributing to delayed recovery, prolonged opioid exposure, and risk of chronic postsurgical pain. Machine learning has been suggested as a strategy to improve postoperative pain management by predicting pain trajectories, estimating opioid requirements, identifying patients at risk for poor outcomes and improving the clinical management. This critical review examines current applications of machine learning in postoperative pain, including preoperative risk stratification, intraoperative and postoperative prediction, objective assessment, wearable monitoring, discharge opioid prescribing, and use in specialized populations.We found that although many models demonstrate moderate to strong predictive performance, their clinical utility remains uncertain. Most studies are retrospective, single-center, or procedure-specific, with limited external validation and heterogeneous outcome definitions. While many models incorporate postoperative variables, this influences the stage of care at which they can be applied, making them more useful for guiding postoperative management than for informing preoperative or intraoperative interventions. As a result, future work should move beyond prediction accuracy toward prospective validation, actionable hypothesis driven multi-modal models, and large-scale trials that determine whether machine learning–guided care improves pain control while reducing unnecessary opioid exposure.