This paper presents a hybrid approach combining CFD-trained machine learning models and sensor data to enable fast, real-time estimation of indoor pollutant dispersion with low prediction error, and has been part of IEEE DCOSS-IoT 2025 (International Conference on Distributed Computing in Smart Systems and the Internet of Things).