Accurate air quality assessments are essential for understanding exposure to pollution and supporting environmental policy decisions. This study presents a comprehensive methodology for estimating air quality index (AQI) using satellite data and Google Earth Engine (GEE). The research process begins with identifying shortcomings in current approaches to air quality monitoring and reviewing relevant literature on satellite-based pollutant estimation and AQI calculation. To simplify the analysis, a dedicated application has been developed in GEE to enable dynamic selection of study areas and integration of satellite column density data. To estimate surface concentrations at ground level, the pollutant columns extracted from the satellite are processed through molecular weight conversion and atmospheric scaling. The concentration is then standardised from mol/m² to μg/m³ to facilitate the calculation of the AQI. For key air pollutants, individual air quality indicators are generated to provide insight into spatial patterns of air quality in the study area. This methodology demonstrates a scalable and reproducible approach to satellite-based air quality monitoring and provides the basis for future improvements, including higher-resolution datasets, predictive modelling, and integration with terrestrial measurements.