Hospital systems are currently dealing with a large patient influx brought on by a number of circumstances, including seasonal flows or epidemic-related health problems. Hospital facilities, especially emergency departments (EDs), have to admit people for medical care regardless of the severity of the care requirements. However, the large patient volume frequently results in longer length of stay (LOS) for patients and causes issues with congestion in emergency departments. Hospital administrators must forecast the patient’s length of stay (LOS), as this is a crucial metric for evaluating ED congestion and the utilization of medical resources (allocation, planning, and utilization rates). Thus, to enhance ED management, precise LOS prediction is required. In this study, a logistic regression algorithm (LRA) model-based method for forecasting patient length of stay (LOS) in the ED is proposed. Without making any presumptions about the distribution of the data, it flexibly learns pertinent information from linear and nonlinear processes and greatly improves prediction accuracy. In order to facilitate decision-making and avoid crowding, we also categorized the anticipated patients’ length of stay (LOS) based on the amount of time they spent in the pediatric emergency department (PED). The PED provided real data that was used in the trials. We compared the LRA results with those from other models. The results show that LRA performs better than the other models and attest to the models’ suitability for forecasting patient length of stay.