High-resolution data of fine particulate matters (PM_2.5) are of great interest for air pollution prevention and control. However, due to the uneven spatial distribution of ground stations, satellite acquisition cycle, and cloud/rain, high-resolution products cannot be provided on a complete spatio-temporal scale. To provide a full daily PM_2.5 product in recent years at 1-km grid of the Beijing-Tianjin-Hebei (BTH) region, here we apply a state-of-the-art machine learning approach, CatBoost, to (1) reconstruct satellite aerosol optical depth (AOD) data; and to (2) estimate gridded PM_2.5 from station measurements combining elevation, meteorological factors, and the reconstructed AOD data. Compared with existing approaches, CatBoost substantially improved the performance of AOD reconstruction by ~16%. We further show that a wavelet decomposition procedure on the station-based PM_2.5 and input variables is helpful to improve the estimation accuracy. Overall, the approach has a good performance in estimating PM_2.5 with a cross-validation R² of 0.88 and root-mean-squared error of 17.79 μg/m³. From the new dataset, population-weighted PM_2.5 revealed heterogeneous spatial distribution of exposure in different areas, consistently higher in the Southern and Eastern BTH and lower in Beijing and Northern BTH. In recent years, both AOD and PM_2.5 in the BTH region had notable interannual decreases, which can be attributed to the emission reduction efforts and interannual natural variabilities.