Atmospheric pollution associated with carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) represents an important environmental and public health concern in Andean regions influenced by urban growth, transportation, agricultural burning, mining-related activities, and complex topography. This study analyzed the spatiotemporal variability of CO, NO2, and SO2 in the Apurimac region, Peru, during 2020–2023 using Sentinel-5P/TROPOMI satellite products processed in Google Earth Engine. The original column-density data, expressed in mol/m2, were converted into column-derived estimated concentrations using a simplified effective lower-atmospheric layer height of 2,000 m. These values were used only as relative indicators and were not interpreted as direct ground-level air quality measurements. The results showed heterogeneous spatial patterns across Apurimac. CO presented relatively higher values mainly in Abancay, Chincheros, and Andahuaylas, while NO2 showed higher relative values in Cotabambas, Grau, Abancay, and Chincheros. SO2 exhibited a more irregular and uncertain behavior, with localized positive values, negative retrievals, and high variability, indicating greater sensitivity to satellite retrieval noise. Therefore, SO2 was interpreted as an exploratory indicator rather than robust evidence of province-level pollution hotspots. Temporal analysis showed seasonal fluctuations and isolated peaks, especially during dry-season months. Trend detection was performed using the Mann-Kendall test and Sen’s slope estimator. CO showed a statistically significant decreasing trend, NO2 showed a weak but statistically significant increasing trend, and SO2 did not show a statistically significant trend. Overall, Sentinel-5P/TROPOMI and Google Earth Engine proved useful for identifying relative pollutant patterns in a data-sparse Andean region. Future studies should integrate satellite observations with in situ measurements, meteorological data, boundary-layer information, emission inventories, and independent datasets to improve validation, source attribution, and regional air quality assessment.