Background-Accurately estimating operative case-time duration is critical for optimizing operating room utilization. Current estimates are inaccurate and prior models include data not available at the time of scheduling. Our objective was to develop statistical models in a large retrospective dataset to improve estimation of case-time duration relative to current standards. Study Design-We developed models to predict case-time duration using linear regression and supervised machine learning (ML). For each of these models, we generated: 1) service-specific models and 2) surgeon-specific models in which surgeons were modeled individually. Our dataset included 46,986 scheduled surgeries performed at our center from January 2014 to December 2017, with 80% used for training and 20% for model testing/validation. Predictions derived from each model were compared to our institutional standard. Models were evaluated based on accuracy, overage (case duration > predicted + 10%), underage (case duration < predicted-10%), and the predictive capability of being within a 10% tolerance threshold. Results-The ML algorithm resulted in the highest predictive capability. The surgeon-specific model was superior to the service-specific model, with higher accuracies, lower percentage of overage and underage, and higher percentage of cases within the 10% threshold. The ability to predict cases within 10% improved from 32% using our institutional standard to 39% with the ML surgeon-specific model. The majority of the information utilized in the models was based on procedure and personnel data rather than patient health status.