In order to further improve the prediction accuracy of the air quality index, a hybrid genetic ant colony algorithm is proposed to optimize the back propagation (BP) neural network, so as to predict the air quality index. First, the pheromone distribution of the ant colony algorithm is initialized, and crossover and mutation operations of the genetic algorithm are performed if fitness conditions are not met. Then the state transition probability and pheromone concentration of the ant colony are calculated. When the fitness meets the conditions, the optimal results are used as the optimal weights and thresholds of the BP neural network to improve the shortcomings of a single BP neural network. Finally, historical daily data of the air quality index in Xi’an are utilized for verification, and the experiment shows that all evaluation indexes of the model proposed in this study have smaller errors than those of other comparative models and are more convincing in terms of prediction accuracy. Therefore, the proposed model can effectively predict the air quality index.