People spend nearly 90% of their time indoors, so indoor air quality (IAQ) plays a major role in human health. While many tools exist to monitor and predict outdoor air pollution, they often do not extend to indoor estimations. This study develops a computational method to estimate indoor air quality and human exposure to air pollution, based on a simulation tool that can estimate air exchange between indoor and outdoor environments and the transfer of air pollutants. The models are built using real-world building data and simulate airflow based on wind, temperature, and other environmental conditions. By combining these simulations with outdoor air pollution data and typical human behavior patterns, the study estimates how much pollution people are exposed to indoors in a large scale (community/city). The framework can be adapted for different building types and locations, helping improve understanding and management of indoor air quality across a range of environments.