Accurate prediction of PM_2.5 concentration is essential for public health and environmental protection, but its nonlinearity, variability, and complexity make it difficult. Based on this, this study proposes a gene expression programming algorithm based on virus evolution (VE-GEP) to predict PM_2.5 concentration in response to the shortcomings of traditional GEP. The algorithm introduces a resurrection mechanism and a mutagenic restart mechanism based on GEP. The resurrection mechanism removes poor-quality individuals from the population and improves individual quality in the population. The mutagenic restart mechanism increases population diversity and enhances algorithm optimization-seeking ability by introducing high-quality genes and new individuals. Experimental results show that the VE-GEP algorithm improves the prediction models to different degrees compared to GEP, DSCE-GEP, and CNN-LSTM in spring, summer, and fall, with improvements in the fitness of 1.28%/0.1%/0.13%, 1.86%/1.29%/0.42%, and 0.57%/0.24%/0.29%, respectively, which provides new ideas and methods for PM_2.5 concentration prediction studies.