Marine Litter Aggregation Forecast presents a new estimation of long-term global distribution of marine litter proceeding from land-based and from sea-based sources at global scale, taking advantage of numerical reanalysis databases of historical met-ocean conditions. Different machine learning techniques are applied to generate long-term climate-based series of those environmental variables that may affect the drift of marine litter over the sea surface such as currents, wind and wave-induced Stokes drift. The generated series feeds a state-of-the-art Lagrangian model in order to simulate the global long-term evolution of marine litter transport through the ocean surface. As marine debris sources coastal cities, river outputs and shipping routes are considered. Besides the main goal of the project, the proposed methodology has also the potential for interesting secondary achievements, such as providing the estimation of global scale marine litter distribution for specific past dates and predicting the expected future location and distribution few months ahead.