Abstract: The precise identification of the spatiotemporal characteristics and influencing factors of the synergistic effect of pollution and carbon reduction (SEPCR) in urban areas holds profound theoretical and practical significance for implementing the national "dual-carbon" strategy, comprehensively advancing green transformation, and enhancing the efficacy of eco-environmental governance. Focusing on 284 prefecture-level cities in China from 2014 to 2023, this study integrated the entropy weight-TOPSIS method, the coupling coordination degree model, and the super-efficiency SBM model to measure the SEPCR level. Furthermore, it employed kernel density estimation and spatial Markov chains to analyze its spatiotemporal characteristics, utilized the combined MIC-PIRF-RFE model to screen core influencing factors, and applied the XGBoost-SHAP model to reveal the nonlinear threshold effects and spatial heterogeneity of each factor on SEPCR. The results showed that from 2014 to 2023, the SEPCR level in the cities generally exhibited a steady upward trend, with the mean value increasing from 0.1451 to 0.1789, and the spatial pattern presented a stepped distribution of "the East leading, the Central and West catching up, and the Northeast fluctuating". SEPCR displayed significant spatial dependence and path locking characteristics. Low- and medium-level cities faced resistance in upward mobility, and regional development exhibited obvious spatial inertia. Population density, urbanization rate, and technological innovation were the three core influencing factors of urban SEPCR, each showing significant nonlinear characteristics: population density regained scale dividends after crossing a specific threshold, urbanization rate exhibited phased inhibition due to agglomeration of energy-intensive activities, and technological innovation continued to exert a positive driving effect. The influencing factors and their threshold effects exhibited significant regional heterogeneity. In the eastern region, fixed asset investment turned into a positive driver after crossing the threshold, but high-level technological innovation showed an inhibitory effect. The central region was constrained by population carrying capacity and presented obvious "technology rebound" and "pollution haven" effects. The western region was in a state of low-level lock-in regarding its opening up. The northeastern region needed to cross a high investment threshold to break through the initial negative constraints. Differentiated regulations should be implemented in each region based on its specific threshold interval.