The emission parameterization is a crucial part of numerical pollen dispersion models. This paper shows that Artificial Neural Networks (ANNs) can substantially improve the performance of the Ambrosia pollen emission in numerical pollen dispersion models such as COSMO-ART. Based on simultaneous measurement of Ambrosia pollen concentrations and meteorological variables in the source area, ANNs were trained to predict the diurnal profile of pollen emission. Six different combinations of explanatory meteorological variables were trained with five different ANN configurations resulting in 30 candidate emission models. The best network configuration for each combination of explanatory variables were used as emission parameterization in the numerical pollen dispersion model COSMO-ART. In addition, two benchmarks were implemented: an emission parameterization based on sigmoid functions and an artificial neural network using only time as an explanatory variable. The Ambrosia pollen seasons of 2015 and 2016 were simulated using the two benchmarks and the six emission parameterizations. The modelled diurnal profile of emission fluxes at 15 different sites from Serbia, Hungary and France with strong local pollen sources were compared with observed concentrations. Artificial Neural Networks based emission parameterization substantially improved the performance of the Ambrosia pollen emission in COSMO-ART compared to the emission based on the sigmoid functions in all these three countries. However, a time-related explanatory variable must be used. This suggests that the ANN-based emission parameterizations can be used at distant locations as well. On the other hand, the use of meteorological related parameters did not increase the performance compared with the time-only benchmark.