Posidonia oceanica is an endemic seagrass species in the Mediterranean. Even though this species has been put under protection, P. oceanica is currently listed as threatened. Therefore, in order to conserve this species, high resolution, accurate and temporal distribution maps are needed to be produced. In this study, it is aimed to create seagrass distribution maps with machine learning algorithms namely as random forests and support vector machines using WorldView-2 imagery. In-situ data has been collected via underwater video and scuba diving for classification training and testing. Atmospheric, radiometric and water column corrections are applied for preprocessing of the optical satellite image. The light penetration in the water is limited by depth. Therefore, we have limited our study area based on maximum depth of 20 meters. The classification accuracies and Cohen's kappa coefficients are calculated as 94% and 0.89 for random forests, 71% and 0.61 for support vector machines, respectively. According to the results, it can be clearly said that random forests method is superior to support vector machines for seagrass mapping in our study area. The proposed framework in this study enables to rapidly produce seagrass distribution maps which can be used to monitor temporal change for a sustainable ecosystem.