Predicting patient length of stay (LoS) is crucial for optimizing resource allocation and enhancing healthcare efficiency. However, achieving accurate LoS predictions remains a challenging and complex task. This study presents a non-disease-specific predictive model that integrates machine learning (ML) methods and Bayesian inference techniques to accurately predict hospital LoS using static patient admission data. While traditional statistical regression techniques have been widely used for LoS prediction within hospital settings, this research investigates the capabilities of ML and Bayesian inference algorithms in this context. By leveraging Bayesian inference techniques, our model captures complex relationships within the data and quantifies uncertainty, offering a more nuanced understanding of the outcomes. This methodological approach offers a more comprehensive and probabilistically grounded framework for LoS prediction, allowing more informed decision-making in resource allocation and patient management. Among the evaluated models, extreme boosting and support vector machine regressor models demonstrated the highest efficiency, achieving mean squared logarithmic error (MSLE) values of 0.23 and 0.24, respectively. The Bayesian model also showed competitive performance with an MSLE of 0.25. While it did not outperform other models in terms of error metrics, the Bayesian model’s ability to provide additional uncertainty output enhances its utility, offering valuable supplementary information for informed decision-making. This research highlights the potential of ML and Bayesian inference in predicting patient LoS, emphasizing their significance in effective resource allocation and patient care management within the healthcare sector.