Effective hospital resource management hinges on established metrics such as Length of Stay (LOS) and Prolonged Length of Stay (pLOS). Reducing pLOS is associated with improved patient outcomes and optimized resource utilization (e.g., bed allocation). This study investigates several Machine Learning (ML) models for both LOS and pLOS prediction. We conducted a retrospective study analyzing data from general inpatients discharged between 2022 and 2023 at a northern Italian hospital. Sixteen regression and twelve classification algorithms were compared in forecasting LOS as either a continuous or multi-class variable (1–3 days, 4–10 days, >10 days). Additionally, the same models were assessed for pLOS prediction (defined as LOS exceeding 8 days). All models were evaluated using two variants of the same dataset: one containing only structured data (e.g., demographics and clinical information), and a second one also containing features extracted from free-text diagnosis. Ensemble models, leveraging the combined strengths of multiple ML algorithms, demonstrated superior accuracy in predicting both LOS and pLOS compared to single-algorithm models, particularly when utilizing both structured and unstructured data extracted from diagnoses. Integration of ML, particularly ensemble models, has the potential to significantly improve LOS prediction and identify patients at high risk of pLOS. Such insights can empower healthcare professionals and bed managers to optimize patient care and resource allocation, promoting overall healthcare efficiency and sustainability.