This systematic review examines the recent use of artificial intelligence, particularly machine learning, in operating room management. A total of 22 studies from February 2019 to September 2023 were analyzed. The review highlights the significant 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 shown effectiveness in improving prediction accuracy and resource utilization. However, challenges like data access and privacy concerns are acknowledged. The review emphasizes the evolving nature of AI in perioperative medicine and the need for continued innovation to harness AI's potential for healthcare administrators, practitioners, and patients. AI integration in operating room management promises to enhance healthcare efficiency and patient outcomes. The review also discusses the application of machine learning in predicting PACU length of stay and surgical case cancellations, highlighting the importance of accurate predictions for efficient resource allocation and improved patient care. Challenges such as data quality, model validation, and clinical adoption are addressed, emphasizing the need for further research and collaboration to overcome these barriers. The review concludes that AI, particularly machine learning, has significant potential to transform operating room management, but its successful implementation requires addressing technical, ethical, and practical challenges.This systematic review examines the recent use of artificial intelligence, particularly machine learning, in operating room management. A total of 22 studies from February 2019 to September 2023 were analyzed. The review highlights the significant 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 shown effectiveness in improving prediction accuracy and resource utilization. However, challenges like data access and privacy concerns are acknowledged. The review emphasizes the evolving nature of AI in perioperative medicine and the need for continued innovation to harness AI's potential for healthcare administrators, practitioners, and patients. AI integration in operating room management promises to enhance healthcare efficiency and patient outcomes. The review also discusses the application of machine learning in predicting PACU length of stay and surgical case cancellations, highlighting the importance of accurate predictions for efficient resource allocation and improved patient care. Challenges such as data quality, model validation, and clinical adoption are addressed, emphasizing the need for further research and collaboration to overcome these barriers. The review concludes that AI, particularly machine learning, has significant potential to transform operating room management, but its successful implementation requires addressing technical, ethical, and practical challenges.