This systematic review examines the recent use of artificial intelligence, particularly machine learning, in operating room (OR) management. The review analyzes 22 studies published between February 2019 and September 28, 2023, focusing on the impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit (PACU) resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as data access and privacy concerns are acknowledged. The review highlights the evolving nature of AI in perioperative medicine research and the need for continued innovation to harness AI's transformative potential for healthcare administrators, practitioners, and patients. Ultimately, the integration of AI in OR management promises to enhance healthcare efficiency and patient outcomes.This systematic review examines the recent use of artificial intelligence, particularly machine learning, in operating room (OR) management. The review analyzes 22 studies published between February 2019 and September 28, 2023, focusing on the impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit (PACU) resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as data access and privacy concerns are acknowledged. The review highlights the evolving nature of AI in perioperative medicine research and the need for continued innovation to harness AI's transformative potential for healthcare administrators, practitioners, and patients. Ultimately, the integration of AI in OR management promises to enhance healthcare efficiency and patient outcomes.