This study uses tree-based machine learning (ML) to forecast PM_2.5 in a complex terrain region. The models show the potential to predict pollution events with several hours of anticipation, and they integrate multiple sources of information, including in situ stations, satellite data, and deterministic model outputs. The importance analysis helps understand the processes affecting air quality in the region and highlights the relevance of external sources of pollution in PM_2.5 predictability.