Based on the panel data of 2383 counties in China from 2003 to 2022, the composite system synergy model and super-efficient SBM-DEA model were initially employed to quantify the synergistic reduction of pollution and carbon emissions. Subsequently, the spatiotemporal evolution patterns of synergistic reduction of pollution and carbon emissions in counties were explored by kernel density estimation, spatial autocorrelation analysis, and standard deviation ellipse. Ultimately, the XGBoost algorithm and SHAP value interpretation algorithm were combined to identify the main influencing factors of synergistic reduction of pollution and carbon emissions. The results show that. the level of synergistic reduction of pollution and carbon emissions in Chinese counties has been gradually rising, with a marked acceleration observed after 2020. The synergistic reduction of pollution and carbon emissions exhibits a “high in the east, low in the west” pattern, accompanied by a significant spatial positive correlation. The distribution center of synergistic reduction of pollution and carbon emissions in counties generally migrates to the southeast, reflecting a north-south centripetal trend and an east-west spatial divergence. Energy intensity is the main influencing factor of synergistic reduction of pollution and carbon emissions, and has a negative impact on the synergistic reduction. Technological innovation and industrial structure generally promote the synergistic reduction. The impact of economic development, population density and financial development on the synergistic reduction shows complex nonlinear characteristics. Compared with the eastern region, the impact of energy intensity on the synergistic reduction of pollution and carbon emissions in the central and western regions is more significant.