The prediction of various physical quantities has emerged as a highly effective tool in the modern scientific landscape. This article presents the design and implementation of an artificial neural network-based predictive model for forecasting particulate matter emissions during solid fuel combustion. The model employs measurements of five parameters: The input data for the model included oxygen, carbon monoxide, nitrogen oxides, carbon dioxide, and air supply velocity. The results of the testing demonstrated that the model exhibited remarkable performance and accuracy, reaching the level of statistical error. In particular, the model demonstrated an excellent performance on independent data, with a root-mean-squared error of 0.034 mg/m3, a model error of 0.06%, a correlation coefficient of 0.975, and a coefficient of determination of 0.951 for predicting particulate matter emissions during combustion processes. These results provide compelling evidence of the efficacy of the selected methodology. Consequently, it can be concluded that the model is well constructed and that its generalizability allows for practical applications.