Coastal water quality management is a public health concern, as water of poor quality can potentially harbor dangerous pathogens. In this study, we employ routine monitoring data of E s c h e r i c h i a C o l i and enterococci across 15 beaches in the city of Rijeka, Croatia, to build machine learning models for predicting E. C o l i and enterococci based on environmental features. Cross-validation analysis showed that the Catboost algorithm performed best with R 2 values of 0.71 and 0.69 for predicting E. C o l i and enterococci, respectively, compared to other evaluated algorithms. SHapley Additive exPlanations technique showed that salinity is the most important feature for forecasting both E. C o l i and enterococci levels. Furthermore, for low water quality sites, the spatial predictive models achieved R 2 values of 0.85 and 0.83, while the temporal models achieved R 2 values of 0.74 and 0.67. The temporal model achieved moderate R 2 values of 0.44 and 0.46 at a site with high water quality.