Limited research has assessed the spatio-temporal distribution and chronic health effects of NO2 exposure, especially in developing countries, due to the lack of historical NO2 data. A gap-filling model was first adopted to impute the missing NO2 column densities from satellite, then an ensemble machine learning model incorporating three base learners was developed to estimate the spatiotemporal pattern of monthly mean NO2 concentrations at 0.05° spatial resolution from 2005 to 2020 in China. Further, we applied the exposure dataset with epidemiologically derived exposure response relations to estimate the annual NO2 associated mortality burdens in China. The coverage of satellite NO2 column densities increased from 46.9% to 100% after gap-filling. The ensemble model predictions had good agreement with observations, and the overall, temporal and spatial cross-validation (CV) R2 were 0.88, 0.82 and 0.73, respectively. In addition, our model can provide accurate historical NO2 concentrations, with both by-year CV R2 and external separate year validation R2 achieving 0.80. The estimated national NO2 levels showed a increasing trend during 2005-2011, then decreased gradually until 2020, especially in 2012-2015. The estimated annual mortality burden attributable to long-term NO2 exposure ranged from 305 thousand to 416 thousand, and varied considerably across provinces in China. This satellite-based ensemble model could provide reliable long-term NO2 predictions at a high spatial resolution with complete coverage for environmental and epidemiological studies in China. Our results also highlighted the heavy disease burden by NO2 and call for more targeted policies to reduce the emission of nitrogen oxides in China.