Accurate identification of patient emergency states in emergency rooms is vital for delivering timely and appropriate medical intervention. This paper presents a comprehensive approach using a dataset from a Northern Italian hospital to predict patient urgency levels with machine learning algorithms. We processed and analyzed the data through feature selection techniques, feature importance analysis, and interpretability methods, focusing on the dataset dimensions. Our tests resulted in accuracy exceeding 95% in three machine learning algorithms, demonstrating the feasibility of developing an intelligent computerized system capable of predicting emergency states in an emergency room setting. These findings suggest that integrating advanced data analytics can significantly enhance patient triage and hospital resource planning.