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The evolution of the power industry toward large-scale automation and selfmonitoring provides the opportunity to optimize the technical and environmental performance of the plant with data-driven methods with little changes in infrastructure. This article applies the artificial neural network (ANN) and genetic algorithm (GA) to predicting and optimizing NO emissions. Multiple linear regression models, correlation matrix, and research background are employed to find the most influential input features. The generated power, natural gas flow, the flow of gas recirculation fan, gas air heater temperature, and the amounts of oxygen in the stack are identified as the effective input features. Mean Square Error (MSE) and the coefficient of determination (R 2 ) of best architecture (22 neurons in a hidden layer) are calculated 0.0117 and 0.96651, respectively. The Average Percentage Error (APE) is usually below 10%, meaning the model is in good agreement with real data. Finally, the Genetic Algorithm (GA) is used to minimize the amount of NO emissions. The ANN-GA techniques reduce the NO emission by at least 32% for selected records, enabling us to optimally find the prominent features affecting NO emission by the operational conditions and low economic costs.