Emergency Department (ED) physicians are faced with complex care settings, including a high level of uncertainty and intensity. Burnout among physicians is increasing every year, and ED physicians are one of the groups most prone to burnouts and work-related stress in the US. This research focused on developing a supervised Long Short-Term Memory (LSTM) artificial neural network model to predict a physician’s stress level based on their physiological data. Twelve attending physicians working a 3:00 pm - 11:00 pm shift at Greenville Memorial Hospital (GMH) in Greenville, SC, participated in the study. Stress levels were estimated using physiological measures, including heart rate and electrodermal activity. Over 100 hours of physiological data were collected from 12 eight-hour shifts. Initially, an 80:20 split was used on the 12 individual datasets for training and testing the model. Further, to develop a generalizable model, the data were merged, and a 60:20:20 split was used for training, validating, and testing the model. On the test set, the model achieved values of 0.98, 0.17, and 0.005 as the R-squared, RMSE, and loss for EDA data and 0.99, 0.41, and 0.002 for HR data.