In recent years, ships are expected to improve energy efficiency and reduce carbon emissions. For naval vessels, it is important to be able to maintain their mission profile. It is therefore required to provide real-time advice to the ship’s crew on the optimal speed and propulsion mode settings that include the actual environmental conditions. This paper proposes a novel machine learning approach to establish the ship’s fuel consumption per mile for the actual environmental conditions and develop fuel consumption curves for the various propulsion configurations. The proposed approach uses a multi-layer perceptron (MLP) model to establish the ocean parameters based on the own ship data, with an accuracy of 4.150 %. Using these ocean parameters combined with the own ship data, the fuel per mile is predicted and fuel consumption curves are established using an MLP-model, with an accuracy of 1.551 %. This thesis shows that the proposed approach makes it possible to help a ship’s crew make well informed decisions to reduce their CO2 emissions in real time while still meeting their mission profile. The proposed approach is especially useful for military operators since there is no need for external data sources. Further research is identified to optimize the proposed approach using a dataset containing a higher variety and number of environmental conditions and propulsion modes to improve and validate the fuel consumption curves for the entire spectrum of speeds, environmental conditions and propulsion modes.