In order to address the challenges of sensor deployment and vulnerability in harsh gas turbine environments, a predictive model based on deep neural networks is developed. The model forecasts nitrogen oxide concentrations, which reflect operational performance and modes. A first-stage multi-scale fusion approach was integrated for overall trend prediction. A second-stage design using wavelet transforms and feature convolution was employed for detailed information prediction. The results show that the two-stage model achieves a mean absolute error of 0.681 and a root mean square error of 0.995. This demonstrates the model’s ability to identify operational patterns of gas turbines. It also captures data fluctuations under adverse conditions. Therefore, the model accurately predicts nitrogen oxide emission concentrations.