Accurate modelling of the combustion process is essential for improving efficiency and performance and to reduce emissions. In recent years, alongside significant advances in artificial intelligence, the use of machine learning for engine design and combustion analysis has grown, with the potential to significantly reduce the number of experimental tests required and, at the same time, the computational costs associated with numerical simulations both 1D models and 3D CFD, which could be very expensive. The objective of this thesis is to develop and validate a model based on artificial neural networks for predicting burn rate curves in a two-stroke marine diesel engine, starting from a selected set of operating variables.