This study explored machine learning’s potential in predicting the nutritional status and outcomes for pneumonia patients. It focused on 4,368 patients in a Taiwan medical center from Jan 2016 to Feb 2022, excluding ICU cases. The average age was 77.6 years, with 10.2% well-nourished, 76.3% at-risk, and 13.5% malnourished. Machine learning models, particularly LightGBM and XGBoost, showed high accuracy in predicting hospital stays, mortality rates, and readmissions. These findings emphasize the role of data-driven methods in enhancing patient care and managing conditions like pneumonia more effectively.