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The Bay of Biscay is being affected by increasing level of marine litter, which is causing a wide variety of adverse environmental, social, public health, safety and economic impacts. The term "beach littering" has been coined to refer to the marine litter that is deposited on beaches. This litter may come from the sea and through land-based pathways, either from remote or adjacent areas. Dirty beaches can derive in loss of aesthetical value, beach cleaning cost, environmental harm or tourism revenue reduction among others. Therefore, local authorities have started to search for cost-effective approaches to understand and reduce litter accumulation in their beaches. A model is presented in this paper, which is based on Bayesian Networks and enables the forecasting of marine litter beaching at seven beaches located on the south-eastern coast of the Bay of Biscay. The model uses 9.5 years of metocean, environmental and beach cleaning data. The class to predict was defined as a variable with two possible values: Low and High accumulation of beach litter. The obtained models reached an average accuracy of 65.3 ± 6.4%, being the river flow, precipitation, wind and wave the most significant predictors and likely drivers of litter accumulation in beaches. These models may provide some insight to local authorities on the drivers affecting the litter beaching and may help to define their strategies for its reduction. • A model for beach litter accumulation understanding and forecasting designed. • The model used beach litter and metocean data to forecast the amount of litter. • River flow, precipitation, wave and wind were the top predictors of beach littering. • Predictive models had a competitive performance of approx. 65% success rate. [ABSTRACT FROM AUTHOR]