Atmospheric particulate matter (PM) significantly influences air quality, human health, and regional climate dynamics, particularly in rapidly developing regions such as South India. This study investigates spatial and seasonal variations of PM concentrations by integrating satellite-derived Aerosol Optical Depth (AOD) with ground-station PM2.5 measurements. MODIS AOD data were processed alongside Central Pollution Control Board (CPCB) datasets to evaluate the strength of correlations across multiple urban and semi-urban locations. A meteorology-normalized regression framework was developed to quantify PM variability and improve the accuracy of the AOD-PM2.5 relationship. Hypothetical results indicate strong positive correlations during winter and post-monsoon seasons, driven by boundary-layer suppression and increased anthropogenic emissions. The proposed methodology demonstrates that remote sensing can serve as a scalable complement to sparse ground-based monitoring networks, providing reliable insights into particulate matter dynamics in South India.