China is actively pursuing synergistic governance to address air pollution and carbon mitigation issues. This study, focusing on concentration as a key feature, assesses the synergy performance in the Yangtze River Delta Urban Agglomeration (YRDUA), revealing fluctuating trends with only seven cities showing improvement. To further understand the influences from local drivers, we employed an explainable spatial machine learning approach, integrating Geographical Weighted Regression (GWR), Random Forest (RF), and Shapley Additive Explanation (SHAP) to capture nonlinear, threshold, and interaction effects among explanatory variables. The analysis identifies longitude, SO₂ emissions from industrial sources, wind speed, latitude, and the proportion of GDP from tertiary sector as the top five influencing factors, emphasizing the importance of geographical position, local air pollution emission, and meteorological condition. Most drivers exhibit nonlinear impacts and interactions with clear thresholds. Such as, wind speeds, exceeding 9.3 m/s negatively impact synergy. Furthermore, spatial heterogeneity of drivers’ influence is evident across cities and provinces. Specifically, cities along the eastern coast benefit from geographical advantages that enhance synergy in air quality improvement and carbon mitigation. Meteorological conditions, especially wind speed, significantly influence synergy, with notable differences between northern and southern coastal cities. These findings underscore the need for locally tailored governance strategies that leverage each city’s unique geographical and socioeconomic attributes to enhance synergistic governance effectiveness. This research contributes to understanding the complex interplay of local drivers influencing synergistic governance in the YRDUA, providing valuable insights for policymakers aiming to improve air quality and promote sustainable development in rapidly urbanizing regions.