Nowadays, it is fundamental to have fast and reliable virtual tools to accelerate the development of robust and efficient engines, in order to meet the increasingly stringent regulations introduced by the European Commission on both pollutant and greenhouse gas (GHG) emissions. In particular, when an ultra-lean dual-dilution approach is implemented, as in this project, conventional 0D/1D CFD simulation software can be difficult and highly time-consuming to calibrate for achieving a reliable combustion model. Therefore, Machine Learning (ML) and Artificial Neural Network (ANN) can represent a valid alternatives to predict the combustion profile under these challenging conditions. The aim of this thesis is to establish three different ML and ANN models for a ultra lean dual-dilution engine, based on experimental data obtained during the development of the PHOENICE (PHev towards zerO EmissioNs & ultimate ICE efficiency) H2020 project.