The aim of this thesis is the development of a mathematical and numerical model of an automotive diesel engine, based on the use of artificial neural networks as an alternative to traditional physics-based models implemented in simulation software such as GT-Power. The proposed model, which operates as a black-box representation of complex engine phenomena, was trained using data generated by GT-Power simulations, considered in this context as the physical reference (ground truth) for validating engine behavior. This approach fits into a broader methodological innovation framework aimed at optimizing and improving the efficiency of existing engine technologies, in line with a realistic, gradual, and sustainable energy transition.