PM_2.5 is the main source of air pollution in China. The problem of air pollution has become the focus of public opinion and academic research in recent years. This article departs from the traditional single-scale approach and adopts a spatial multiscale perspective. Leveraging annual average PM_2.5 concentration data and urban socioeconomic data spanning the period from 2009 to 2018, in conjunction with atmospheric PM_2.5 remote sensing inversion datasets, a comprehensive analysis is conducted. This analysis encompasses the utilization of GIS spatial-temporal analysis techniques and geographic detectors. The primary objective of this research is to investigate the spatiotemporal evolution characteristics of PM_2.5 in the Yangtze River Basin during the years 2009 to 2018, as well as to elucidate the influencing factors therein. This study is crucial to the joint prevention and control of air pollution. The Results showed that (1) The overall trend in the number of cities with annual average PM_2.5 concentrations below 35 μg/m³ (the annual limit value in China) exhibits fluctuating upward dynamics. (2) From 2009 to 2018, the low-value area distribution of the annual average PM_2.5 concentration in the Yangtze River Basin was stable, whereas the high-value area showed a trend of “first decreasing, then increasing, and then decreasing.” (3) The spatial and temporal agglomeration effect was evident, showing an “east-hot, west-cold” agglomeration pattern. From 2009 to 2018, the high-value aggregation area expanded to the middle part of the Yangtze River Basin and then continued to the north. The low-value concentration area was concentrated in the western part of the Yangtze River Basin, and the range change trend was not large. (4) While each variable concurrently engages in interactions, they also exhibit varying degrees of influence on the spatiotemporal distribution of PM_2.5. Among them, population density and the proportion of urban built-up areas in the index layer of population and urbanization are strongly correlated factors.