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Coastal hazards such as flooding and erosion can cause large economic and human losses. Under this threat, early warning systems can be very cost-effective solutions for disaster preparation. The goal of this study was to develop, test, and implement an operational coastal erosion early warning system supported by a particular method of machine learning. Thus, the system combines Bayesian Networks, and state-of-the-art numerical models, such as XBeach and SWAN, to predict storm erosion impacts in urbanized areas. This system was developed in two phases. In the development phase, all information required to apply the machine learning method was generated including the definition of hundreds of oceanic synthetic storms, modeling of the erosion caused by these storms, and characterization of the impact levels according to a newly defined eerosion iimpact index. This adimensional index relates the distance from the edge of the dune/beach scarp to buildings and the height of that scarp. Finally, a Bayesian Network that acted as a surrogate of the previously generated information was built. After the training of the network, the conditional probability tables were created. These tables constituted the ground knowledge to make the predictions in the second phase. This methodology was validated (1) by comparing 6-h predictions obtained with the Bayesian Network and with process-based models, the latest considered as the benchmark, and (2) by assessing the predictive skills of the Bayesian Network through the unbiased iterative k-fold cross-validation procedure. Regarding the first comparison, the analysis considered the entire duration of three large storms whose return periods were 10, 16, and 25 years, and it was observed that the Bayesian Network correctly predicted between 64% and 72% of the impacts during the course of the storms, depending on the area analyzed. Importantly, this method was also able to identify when the hazardous conditions disappeared after predicting potential consequences. Regarding the Regarding the second validation approach, second validation approach, the k-fold cross-validation procedure was applied to the peak of a set of varying storms and it demonstrated that the predictive skills were maximized (63%