The abundance of airborne particulate matter with an aerodynamic equivalent diameter of 2.5 µm or less (PM2.5) is a significant environmental and health issue. Many tools have been used to examine the relationship between PM2.5 abundance and meteorological variables, but some of the relationships are nonlinear, non-Gaussian, and even unknown. Machine learning provides a broad range of practical algorithms to help examine this issue. In this study, we use machine learning to classify the morphology of PM2.5 seasonal cycles in East Asia. Machine learning is able to objectively classify the seasonal cycles and, without a priori assumption, is able to clearly distinguish between urban and rural areas. We show an example of this in the Sichuan Basin of China. Furthermore, machine learning is also able to provide physical insights by identifying the key factors associated with each distinct shape of the seasonal cycle, such as highlighting the key role played by the topography and the built environment.