The field of healthcare is witnessing a significant shift towards leveraging machine learning and artificial intelligence to improve patient outcomes and optimize resource allocation. Recent developments have focused on predicting hospital stay durations, emergency department overcrowding, and disease severity, with a emphasis on developing innovative frameworks and models that can accurately forecast patient flow and support proactive resource allocation. Noteworthy papers in this area have demonstrated exceptional predictive performance, with some achieving accuracy rates of over 90%. The use of semi-supervised learning techniques, hybrid approaches combining clinical data and ultrasound scans, and ensemble methods have shown great promise in advancing the field. Notable papers include: The paper on Severity Classification of Chronic Obstructive Pulmonary Disease in Intensive Care Units, which introduced an innovative machine learning framework for COPD severity classification. The Hybrid Approach Combining Ultrasound and Blood Test Analysis with a Voting Classifier for Accurate Liver Fibrosis and Cirrhosis Assessment achieved an accuracy of 92.5% in detecting liver fibrosis and cirrhosis.