Fuel is one of the highest cost items while operating a ship, and its combustion results in air emissions polluting environments. Finding ways to increase shipping operations efficiency without compromising the provided service quality is necessary for economic and environmental reasons. This study first used data analysis to find hidden information in one-year navigation data of a double-ended ferry operated along the Swedish coast. The case study ferry was operated using both bow and stern engines partly loaded. A new feature of the power ratio is defined to describe the influence of engine power allocation on total fuel consumption. Then, different machine learning methods are used to establish the ship’s total fuel consumption model due to influences of external factors such as wind and sea currents, etc., together with the power ratio. The established machine learning model is used to find the most efficient operation of allocating power to different engines. It shows that, in theory, up to 35% fuel savings can be achieved for the case study vessel. These findings can further aid with the operational planning for the scope of Eco-driving.Double-ended ferries are an alternative to bridges or tunnels for transporting passengers and cars over water. They are used for commuting in big cities like New York (Siferry 2022), London (TRL 2022), and connecting islands along the coast. Double-ended ferries can achieve this task on short routes where maneuverability may be difficult (Waterhouse, 2016), and relieve road congestion (Leung et al., 2017). The objective in maritime transport is to reduce CO2 emissions by at least 40% by 2030 and to pursue efforts to reach 70% reduction by 2050, compared to the 2008 setup by IMO (2018). Maritime authorities also push regional ferries to become more environmentally friendly and save fuel costs. It becomes important to investigate how to operate these vessels to reduce energy consumption. For this purpose, a fundamental problem is establishing a reliable performance model to describe those ferries’ fuel consumption in terms of their operational profiles, such as allocation of engine load, ship speed, etc. Different models have been researched in the maritime community to address this problem.