Ventilation is essential for maintaining indoor air quality and human comfort, particularly in buildings with mechanical ventilation systems. However, modeling the complex relationship between ventilation rate and measurable indoor environmental parameters, such as humidity, poses significant challenges. Current methods for modeling this relationship are often inefficient. For example, data-driven models frequently require sufficient information on ventilation, which may not be available for constant air volume (CAV) systems. On the other hand, simulation programs can be time-consuming and require expert engagement. In this study, we present two simplified calculation models, including a hybrid model developed by the authors, and a well-known model introduced by EN ISO 13788. We compare their performance in evaluating the impact of ventilation rate on indoor humidity. The hybrid model uses a novel analytical method to upgrade a single-input regression model (which considers only outdoor humidity as input) to enable the evaluation of indoor humidity based on both outdoor humidity and ventilation rate. We used the commercial TRNSYS program to model two scenarios: increasing the reference ventilation rate and decreasing the reference ventilation rate. The results showed that both models matched TRNSYS simulations with satisfactory accuracies, but the hybrid model demonstrated superior performance to the EN ISO 13788 model in both scenarios. The hybrid model’s higher accuracy and hybrid features make it better suited for big data analysis and field studies.