Emergency departments are among the most crowded facilities in healthcare premises, where they receive a variety of cases, including critical ones and life-threatening conditions. Arranging the order of received patients and providing timely and efficient care is of utmost importance. This procedure is usually carried out by a nurse who considers the patient’s symptoms and vital signs besides ready resources. Existing literature revealed that there is variability in the accuracy of the triage process inside emergencies for a variety of reasons. Therefore, developing an aid tool based on Machine Learning (ML) algorithms would help mitigate this issue and improve the workflow inside such a crucial setting. This work provides a comparison between several ML-based classifying models that were developed from MIMIC-IV-ED and MIMIC-IV databases. Moreover, it presents insights into hidden patterns that explain some outcomes of subgroups in the examined individuals.