With rapid economic development, the problem of air pollution has become increasingly prominent. Countries have paid attention to PM2.5, one of the main air pollutants, and have gradually addressed this issue. Based on the 2015–2019 air quality data, meteorological data, and aerosol optical depth data from Harbin, China, this study investigated the relationship between PM2.5, a number of influencing factors, and their temporal changes using a machine-learning method. It can be seen from the analysis that the random forest model can predict PM2.5 concentration. In this model, the mean RH and AOD have a high impact on PM2.5 concentration, but there was negligent correlation with PM2.5. The results indicated that the level of PM2.5 pollution continuously decreased from 2015 to 2019, and there were significant seasonal differences in PM2.5 concentration and its variations. In 2019, due to the impact of heating and adverse meteorological conditions, PM2.5 pollution during the heating period increased significantly. This study provides theoretical and data support for the analysis of PM2.5 pollution in Harbin and formulation of air pollution control policies.

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