This study presents a methodological framework for estimating surface nitrogen dioxide (NO₂) concentrations in Mexico City during 2024. Sentinel-5P satellite observations, ERA5 meteorological variables, and ground measurements from the RAMA were integrated to generate high-resolution estimates through a Random Forest model combined with statistical downscaling. After data cleaning, 3246 aligned records were retained. The model achieved robust performance (R² = 0.9196; RMSE = 6.80 µg/m³; MAE = 4.55 µg/m³), demonstrating its ability to reproduce both spatial and temporal variations in NO₂ across the metropolitan area. These results confirm that machine-learning-based downscaling effectively enhances satellite-derived pollution estimates and provides a reliable tool for urban air quality assessment.