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In recent studies, artificial intelligence models have been developed for the prediction of nitrogen oxide emissions from thermal power plants. This research utilizes an artificial intelligence prediction model with more coal input features than boiler features, including volatility, ash content, sulfur content, fixed carbon, total moisture, calorific value, grinding rate, fuel ratio, coal feeding rate, boiler efficiency, total air volume, and excess air volume. The paper delves into the importance analysis of input features for artificial intelligence models. Moreover, feature importance analysis is not only a prerequisite for predicting nitrogen oxide emissions but also a study providing insights into model performance. An artificial neural networks (ANN) regression model is employed to predict nitrogen oxide emissions, and the results demonstrate that the number of feature importance significantly impacts model performance. The best model performance is achieved with eight specific input features. Finally, after the training and validation processes, the ANN model yields the optimal coefficient of determination (R2) value. • Development of an artificial intelligence prediction model for nitrogen oxide emissions from thermal power plants. • A comprehensive set of input features is utilized for emissions prediction. • Importance analysis of input features significantly impacts model performance. • Optimal model performance was achieved with eight specific input features. • The robust coefficient of determination (R2) value demonstrates the accuracy of the ANN model. [ABSTRACT FROM AUTHOR]