A comprehensive understanding of how socioecological factors influence multiple ecosystem services (ESs) may provide stakeholders with a management tool to coordinate economic development and environmental protection. However, previous studies have focused more on the supply side compared with the demand of ESs. Furthermore, the underlying mechanisms for the changes in the supply–demand balance of ESs and their multiple drivers remain unclear. Therefore, in this study, an integrated theoretical framework was developed to assess the interactions of socioecological factors, including land use and cover, the social economy, climate, and topography, with the variations in ES supply, demand, and balance, which were mapped at the city scale in the Yangtze River Economic Belt (YREB). A variance inflation factor (VIF) was employed to detect the multicollinearity of the factors, and the sum of Akaike weights was used to simplify the driving factors and identify essential driving factors. We employed variation partitioning analysis (VPA) to reveal the effects of unique and combined drivers on ES supply, demand and balance. The results revealed that undersupply cities (11.93% of the area) were concentrated within the three national urban agglomerations and increased in area from upstream to downstream, whereas oversupply cities (88.07%) were distributed mainly in the surroundings of the urban agglomerations. According to the VPA, the essential driving factors effectively explain the variation in the ES supply, demand, and balance in the YREB. More importantly, these driving factors were simplified with no significant decrease in explanatory power. In oversupply cities, the ES changes were determined based on socioeconomic factors (urbanization rate and population density), land use/land cover (cropland, woodland, and unused land), and their interactions. In undersupply cities, population density (PD) explained the majority of the variation in ESs. However, the effects of climate and topography on ESs were more prominent at the scale of all cities in the YREB. In addition, PD displayed a significant negative correlation with ES supply and balance, but was positively related to ES demand in the undersupply cities. These findings contribute to a comprehensive understanding of the effects of the interactions among socioecological factors on the supply–demand balance of ESs. This study is informative for human well-being and sustainable socioeconomic development in the region.