Investigations on the chronic health effects of fine particulate matter (PM_2.5) exposure in China are limited due to the lack of long-term exposure data. Using satellite-driven models to generate spatiotemporally resolved PM_2.5 levels, we aimed to estimate high-resolution, long-term PM_2.5 and associated mortality burden in China. The multiangle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD) at 1-km resolution was employed as a primary predictor to estimate PM_2.5 concentrations. Imputation techniques were adopted to fill in the missing AOD retrievals and provide accurate long-term AOD aggregations. Monthly PM_2.5 concentrations in China from 2000 to 2016 were estimated using machine-learning approaches and used to analyze spatiotemporal trends of adult mortality attributable to PM_2.5 exposure. Mean coverage of AOD increased from 56 to 100% over the 17-y period, with the accuracy of long-term averages enhanced after gap filling. Machine-learning models performed well with a random cross-validation R² of 0.93 at the monthly level. For the time period outside the model training window, prediction R² values were estimated to be 0.67 and 0.80 at the monthly and annual levels. Across the adult population in China, long-term PM_2.5 exposures accounted for a total number of 30.8 (95% confidence interval [CI]: 28.6, 33.2) million premature deaths over the 17-y period, with an annual burden ranging from 1.5 (95% CI: 1.3, 1.6) to 2.2 (95% CI: 2.1, 2.4) million. Our satellite-based techniques provide reliable long-term PM_2.5 estimates at a high spatial resolution, enhancing the assessment of adverse health effects and disease burden in China.