Ammonia emissions pose significant challenges for both environmental protection and human health. A substantial portion of these emissions occurs after field fertilization. Accurately predicting these emissions is essential for national inventories and for identifying effective mitigation strategies. Machine learning remains underused in this area. While some static models have been developed to estimate final cumulative emissions, the potential benefits of modeling temporal dynamics to improve these predictions remain unknown. To address this gap, we compared 13 static models (1 random forest, 12 neural networks) and 29 dynamic models (5 random forests and 14 recurrent neural networks) trained and evaluated using cross-validation on a subset of the ALFAM2 dataset. The best-performing model was a recurrent neural network, achieving an average mean absolute error (MAE) of 4.56 kg/ha (95% CI = [4.17, 4.95]) across all test sets. In comparison, the best static neural network and the static random forest yielded average MAEs of 5.28 (CI = [4.86, 5.70]) and 5.54 (CI = [4.94, 6.14]), respectively. The best dynamic random forest model achieved slightly lower performance than the recurrent neural network, with an average MAE of 4.94 (CI = [4.43, 5.46]). These results demonstrate that dynamic models outperform static ones in predicting final cumulative ammonia emissions following fertilization. Finally, the best models were applied to compare different manure application techniques (incorporation, trailing hose, trailing shoe, open slot, closed slot) under 128 scenarios representing various climate conditions and fertilizer types