Natural ventilation is often used as a passive technology to reduce building energy consumption. To leverage the rule-based natural ventilation control to more advanced control at multiple spatial scales, mathematical modeling is needed to calculate the real-time ventilation rate, indoor air temperatures, and velocities at high spatial resolution. This study aims to develop a real-time mathematical modeling framework based on computational fluid dynamics (CFD). The real-time concept is implemented by using real-time sensor data, e.g., wall surface temperatures as boundary conditions, while data assimilation is employed to implement real-time self-calibration. The proof of concept is demonstrated by a case study using synthetic data. The results show that the modeling framework can adequately predict real-time ventilation rates and indoor air temperatures. The data assimilation method can nudge the simulated air velocities toward the observed values to continuously calibrate the model. The real-time CFD modeling framework will be further tested by the real-time sensor data once building construction is fully completed.