Wastewater formed on ships is divided into blackwater and graywater. While blackwater refers to wastewater from toilets, graywater defines wastewater from sinks, laundry and restaurants. Even though some treatments are applied onboard before discharge, wastewater contains significant amounts of fecal bacteria, heavy metals, etc., in excess of water quality standards. Dilution is a secondary natural treatment in the ship-wake region, which occurs after wastewater discharging. According to the Environmental Protection Agency (EPA), the natural treatment process is quantified by dilution factor, which is strongly dependent on vessel width, draft, speed and wastewater discharge rate. In this study, an Artificial Neural Network (ANN) model linked with the main ship parameters was developed to estimate the dilution factors while the ship is in the preliminary design stage. Gross ton, deadweight ton, passenger number, freeboard, engine power, propeller number and block coefficient values of 1041 large cruise ships were used to estimate the likely dilution factors. The best ANN estimation model was determined by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) methods. A decision tree was created for the results and the most important parameters affecting the dilution factors were determined. The main ship dimensions are needed for the dilution factor formulation of EPA whereas in the model created in this study only the gross ton or engine power of the ship is sufficient to estimate the dilution. Moreover, this new model is also usable for the estimation of dilution factors even if the main dimensions of the ship are not known.