Artificial intelligence (AI) can facilitate the prediction and monitoring of air pollution. Benzene, toluene, ethylbenzene, and xylene (BTEX) are harmful aromatic compounds that pose significant health risks. Conventional short-term and sparsely collected stationary monitoring data cannot fully capture the spatial and temporal variations of BTEX. Land use regression combined with machine learning has been shown to enhance the predictive capacity for outdoor BTEX, effectively addressing monitoring gaps. However, because of the scarcity of long-term BTEX measurements, only a few studies have employed this approach to estimate outdoor BTEX. In this study, we developed daily 1 km resolution BTEX models using machine learning, utilizing hourly BTEX measurements from 2011 to2020 at ten monitoring stations across Taiwan. Criteria air pollutants, land use, and meteorological variables were incorporated into the random forest models. Model performance was evaluated using 10-fold cross-validation (CV), and temporal and spatial validation. The CV coefficient of determination (R