This study aims to demonstrate the application of deep learning to quantitatively describe long-term full-scale data observed from wastewater treatment plants (WWTPs) from the perspectives of process modeling, process analysis, and forecasting modeling. Approximately, 750,000 measurements including the influent flow rate, air flow rate, temperature, ammonium, nitrate, dissolved oxygen, and nitrous oxide (N2O) collected for more than a year from the Avedøre WWTP located in Denmark are utilized to develop a deep neural network (DNN) through supervised learning for process modeling, and the optimal DNN (R2 > 0.90) is selected for further evaluation. For process analysis, global sensitivity analysis based on variance decomposition is considered to identify the key parameters contributing to high N2O emission characteristics. For N2O forecasting, the proposed DNN-based model is compared with long short-term memory (LSTM), showing that the LSTM-based forecasting model performs significantly better than the DNN-based model (R2 > 0.94 and the root-mean-squared error is reduced by 64%). The results account for the feasibility of data-driven methods based on deep learning for quantitatively describing and understanding the rather complex N2O dynamics in WWTPs. Research into hybrid modeling concepts integrating mechanistic models of WWTPs (e.g., ASMs) with deep learning would be suggested as a future direction for monitoring N2O emissions from WWTPs.