Machine learning (ML) has emerged as a method to determine patient-specific risk for prolonged postoperative opioid use after orthopedic procedures. : We sought to analyze the efficacy and validity of ML algorithms in identifying patients who are at high risk for prolonged opioid use following orthopedic procedures. : PubMed, EMBASE, and Web of Science Core Collection databases were queried for articles published prior to August 2021 for articles applying ML to predict prolonged postoperative opioid use following orthopedic surgeries. Features pertaining to patient demographics, surgical procedures, and ML algorithm performance were analyzed. : Ten studies met inclusion criteria: 4 spine, 3 knee, and 3 hip. Studies reported postoperative opioid use over 30 to 365 days and varied in defining prolonged use. Prolonged postsurgical opioid use frequency ranged from 4.3% to 40.9%. C-statistics for spine studies ranged from 0.70 to 0.81; for knee studies, 0.75 to 0.77; and for hip studies, 0.71 to 0.77. Brier scores for spine studies ranged from 0.039 to 0.076; for knee, 0.01 to 0.124; and for hip, 0.052 to 0.21. Seven articles reported calibration intercept (range: -0.02 to 0.16) and calibration slope (range: 0.88 to 1.08). Nine articles included a decision curve analysis. No investigations performed external validation. Thematic predictors of prolonged postoperative opioid use were preoperative opioid, benzodiazepine, or antidepressant use and extremes of age depending on procedure population. : This systematic review found that ML algorithms created to predict risk for prolonged postoperative opioid use in orthopedic surgery patients demonstrate good discriminatory performance. The frequency and predictive features of prolonged postoperative opioid use identified were consistent with existing literature, although algorithms remain limited by a lack of external validation and imperfect adherence to predictive modeling guidelines.