Machine-learning algorithms are becoming popular techniques to predict ambient air PM2.5 concentrations at high spatial resolutions (1x1 km) using satellite-based aerosol optical depth (AOD). Most machine-learning models have aimed to predict 24h-averaged PM2.5 concentrations (mean PM2.5). Over Mexico, none has been developed to predict subdaily peak levels, such as the maximum daily one-hour concentration (max PM2.5). We present a new modeling approach based on extreme gradient boosting (XGBoost) and inverse-distance weighting that uses AOD data, meteorology, and land-use variables to predict mean and max PM2.5 in Central Mexico (including the Mexico City Metropolitan Area) from 2004 through 2019. Our models for mean and max PM2.5 exhibited good performance, with overall cross-validated mean absolute errors (MAE) of 3.68 and 9.21 μg/m3 , respectively, compared to mean absolute deviations from the median (MAD) of 8.55 and 15.64 μg/m3. We also investigated applications of our mean PM2.5 predictions that can aid local authorities in air-quality management and public-health surveillance, such as the co-occurrence of high PM2.5 and heat, compliance with local air-quality standards, and the relationship of PM2.5 exposure with social marginalization.