This paper introduces a sensorless approach for data-driven modeling of in-cabin CO2 concentration to optimize air recirculation flap control without the need for a dedicated CO2 sensor. Elevated CO2 concentrations, resulting from passenger exhalation, can impair occupants’ cognitive function and comfort. Current state-of-the-art solutions rely either on time-based control strategies, which lack responsiveness to actual cabin conditions, or on direct CO2 measurements via sensors, which increase system complexity and costs. In contrast, the proposed approach aims to replicate the benefits of sensor-based control without requiring physical sensors. In this study, a model-based methodology is presented, utilizing empirical CO2 measurement data collected from real-world test drives at varying occupancies, fan stages, vehicle speeds, and flap positions. Data acquisition involves a multi-gas analyzer positioned within the passengers’ breathing zone under controlled operation of the vehicle’s climate control unit. Based on these measurements, time-dependent CO2 concentration profiles are represented using exponential functions. These regression curves capture CO2 accumulation, depletion, and balancing behaviors, considering factors such as cabin leakage, pressure differentials at varying speeds, and ventilation conditions. These influences are inherently included in the calibration curves due to their empirical basis. The derived regression curves are implemented into a control model to simulate CO2 concentration throughout the drive, including situations where outside pollution is high and prolonged air recirculation is necessary – such as when driving through tunnels or behind trucks. On the baseline of this simulation, the sensorless control strategy adjusts flap positions accordingly, thereby minimizing both excessive CO2 buildup and unnecessary energy losses due to overventilation. By omitting CO2 sensors and relying solely on existing in-vehicle databus signals, this approach offers a cost-effective solution for cabin air quality management. Future work will focus on real-world validation of the control model and integration of exterior air quality monitoring as a complementary input.