Route and operating optimization of maritime vessels using machine learning techniques The shipping industry handles over 90% of the global trade volume and is responsible for approximately 3% of global COâ emissions. Meanwhile trade by the shipping industry is expected to increase by up to 130% by 2050 compared to 2008. At the same time the goal is to reduce Green House Gas (GHG) emissions from the shipping industry to half of the 2008 level by 2050. In support of this goal this thesis is concerned with a comprehensive approach for optimizing the ships operation i.e. an optimization approach that simultaneously involves route selection energy management propeller pitch and engine control. In addition this thesis also analyses the application of wind propulsion systems. The optimization of the ships operation is implemented in the form of Reinforcement Learning (RL) methods. The use of RL based methods to simultaneously optimize various aspects of the ships trajectory and controls is a novel approach compared to the current state of art and embodies this thesis inherent innovation.The results specifically highlight the importance of parallelizing route optimization with the optimization of other control aspects.Ultimately it is found that the solution emanating from a purely RL based approach can be further enhanced when the optimized route speed and power profiles are used to perform individual DP based optimizations on the energy management in a post processing step