To address the blindness and low efficiency in the inversion of air pollution sources, this study proposes a novel method for air pollution source inversion based on a modified ant colony optimization (M-ACO) algorithm. The Gaussian diffusion model for point source pollution is used to establish the pollution source inversion model which is solved by an ant colony optimization (ACO) algorithm. In view of the shortcomings in the ACO algorithm, the idea of selection and crossover in genetic algorithms is introduced to enrich population diversity, which thus avoids falling into local extrema. At the same time, a reward and punishment factor mechanism is designed to improve the pheromone update rule so that the algorithm can converge faster. It is then summarized as the M-ACO algorithm. Comparative experiments prove that the M-ACO algorithm can make the inversion results of pollution sources more accurate and efficient than the ACO algorithm. It provides effective theoretical support for the practical application of air pollution source inversion.