The aerosol optical depth retrieved from Satellite is basically provides an inexpensive choice for regular observing of particulate matter (PM) concentration, but the challenges present in estimation accuracy. This study developed a physically interpretable Principal Component Analysis (PCA) -regression to estimate PM2.5-related dust column mass density (DCMD-PM2.5) and surface PM2.5 levels in Baghdad using the MERRA-2 aerosol and ERA5 reanalysis data from 2014 to 2025. The meteorological data collected from ERA5 consisted of; temperature, dew point, wind components, cloud cover, precipitation, radiation, and boundary-layer, and aerosol-related variables from MERRA-2; aerosol extinction, black carbon surface mass concentration and scattering indicators. These variables were checked to assess their impacts on DCMD-PM2.5) and surface PM2.5 values. Varimax rotation identified five PCs Baghdad, explaining 75.63% of the total variance and representing thermal-radiative mixing, cloud dynamics, aerosol optical loading, precipitation-related removal, and moisture conditions. The PCs-based regression models were reconstructed using representative driver variables: temperature, high cloud cover, aerosol optical depth (AOD), total precipitation, and dew point. During model development, the PC-based DCMD-PM2.5 model explained 80.6% of the variance, compared with 53.4% for the surface PM2.5 model. Independent validation showed stronger performance for DCMD-PM2.5, with R = 0.799, RMSE = 4.82 × 10⁻⁵ kg m⁻², MAE = 3.86 × 10⁻⁵ kg m⁻², and a very small negative bias. The surface PM2.5 model showed weaker but still informative performance, with R = 0.448, RMSE = 25.29 µg m⁻³, MAE = 20.27 µg m⁻³, and slight positive bias. The results indicate that DCMD-PM2.5 and surface PM2.5 are related but physically distinct aerosol indicators. The proposed framework provides a transparent approach for representing reanalysis-based dust and PM2.5 variability in data-limited urban environments.