Bariatric surgery has emerged as an effective treatment option for individuals with severe obesity, offering not only weight loss but also remarkable improvements in metabolic health and endocrine function. Efficient management of the patient's length of stay (LOS) in the hospital is critical to optimizing healthcare resources and ensuring patient well-being. The objective of this study was to analyze post-operative LOS following bariatric surgery using machine learning (ML) algorithms and determine their predictive performance. Data from 757 patients undergoing bariatric surgery from 2019 to 2022 in a single institution were collected and analyzed. The ML algorithms used included Decision Tree (DT), Random Forest (RF), and Gradient Boosted Trees (GBT). The results showed that RF and GBT had comparable accuracy (71.7% and 71.1% respectively) and outperformed DT (62.0%). RF showed better overall performance, while GBT showed higher precision for predicting shorter LOS (less than 5 days). The results highlight the potential of machine learning algorithms in predicting post-operative LOS, aiding in healthcare resource allocation and personalized patient care.