The purpose of this study was to employ backpropagation neural network (BPNN) and generalized regression neural network (GRNN) techniques to model GPCER generated and emitted from swine deep‐pit finishing buildings as affected by time of day, season, ventilation rates, animal growth cycles, in‐house manure storage levels, and weather conditions. Good results were found which indicated that the artificial neural network (ANN) technologies were capable of accurately modeling source air quality within and from the animal operations. However, it was also found that the process of constructing, training, and simulating the BPNN models was very complex. Thus, the GRNN was characterized as a preferred solution for its use in air quality modeling.