Early identification of patients at risk of prolonged hospital length of stay (LOS) is crucial for optimizing resource allocation and improving care in geriatric settings. We previously developed an ensemble machine learning model to predict prolonged LOS using routinely collected clinical and care-intensity variables. In the present study, we performed a real-world validation of the model within the same institution. Validation was conducted on two macro-groups: (1) a temporally subsequent cohort meeting the original inclusion criteria and (2) a clinically distinct subgroup. Each macro-group was further stratified into three temporal windows (July–December 2023, full year 2024, January-June 2025). Model performance was evaluated across predefined clinical clusters using Accuracy, Positive Predictive Value (PPV), and Negative Predictive Value (NPV). Weighted Generalized Linear Regression models were applied to assess group-by-day interactions and temporal stability. Overall, Accuracy and NPV remained stable across most clusters and validation groups, with no significant interaction between day and group in the majority of analyses. In contrast, PPV demonstrated greater inter-group variability, with significant day-by-group interactions across clusters. Comparative analyses confirmed that differences in PPV were primarily attributable to variations in outcome prevalence and case-mix rather than systematic degradation of model performance. The model maintained robust predictive performance over time and across clinically distinct cohorts within the same institutional setting. While PPV was sensitive to contextual factors, overall accuracy and negative predictive capacity remained stable, supporting the model’s potential utility as a real-time decision support tool for identifying patients at risk of prolonged LOS in geriatric care.