It is of great significance to explore the spatial-temporal variations and estimate the relative importance of the influencing factors of PM_2.5 and O₃ pollution. The study established nationwide surface O₃, NO₂, and SO₂ estimation models using the extreme gradient boosting model and the data fusion method. The cross-validation results indicated that the forecasted models performed well (R-values from 0.86 to 0.95). The results revealed that the pollution levels of O₃, PM_2.5, NO₂, and SO₂ in the North China Plain (NCP) were the highest in China. Subsequently, a multi-task learning model was utilized to estimate the relative importance of influential factors on the PM_2.5 and O₃ pollution in the NCP. The sensitivity analysis results indicated that the O₃ pollution from 2010–2020 in the NCP was susceptible to meteorological factors such as ultraviolet radiation and temperature, as well as anthropogenic precursors such as NO_X, and PM_2.5 pollution in the NCP was constrained by both meteorological factors (44.62%) and anthropogenic emissions (16.86%). The impact of NO₂ on PM_2.5 pollution was similar to its impact on O₃ pollution; therefore, the importance of NO₂ emission reduction to PM_2.5 pollution is as important as that of O₃ pollution, whereas the impact of SO₂ on PM_2.5 was much greater than its impact on O₃ pollution, so SO₂ emission reduction is more important for PM_2.5.