Predicting patient waiting times in public emergency department rooms (EDs) has relied on inaccurate rolling average or median estimators. This inefficiency negatively affects EDs resources and staff management and causes patient dissatisfaction and adverse outcomes. This paper proposes a data science-oriented method to analyze real retrospective data. Using different error metrics, we applied various Machine Learning (ML) and Deep learning (DL) techniques to predict patient waiting times, including RF, Lasso, Huber regressor, SVR, and DNN. We examined data on 88,166 patients’ arrivals at the ED of the Intercommunal Hospital Center of Castres-Mazamet (CHIC). The results show that the DNN algorithm has the best predictive capability among other models. By precise and real-time prediction of patient waiting times, EDs can optimize their activities and improve the quality of services offered to patients.