This article presents an artificial neural network (ANN)-based modeling approach for predicting the performance and emissions of a supercritical coal-fired boiler. The NN model was developed using a large dataset of historical boiler operation data, which include inputs like fuel flow rate, air flow rate, and steam pressure, as well as outputs such as boiler efficiency and emissions of pollutants such as NOx. The results indicate that the NN model is able to accurately predict the performance and emissions of the supercritical boiler, with a high coefficient of determination for the training, validation, and test sets. The results of this study demonstrate the potential of NN-based modeling for improving the efficiency and emissions of supercritical boilers and for providing valuable insights into the complex relationships between the inputs and outputs of these systems. The model presented in the article can be used to answer a question whether it is possible to obtain the same generated power at a higher efficiency or lower emissions using different control signals.