The presence of Escherichia coli in beach sand is directly related to public health outcomes. The physicochemical and wetting properties of sand influence the survival and proliferation of these indicator bacteria. This study is aimed at predicting E. coli concentrations using some of these properties, including the zeta potential, moisture content, Brunauer–Emmett–Teller (BET) surface area, BET pore radius, state of sand, processing temperature and water contact angle of beach sand. For this, the authors developed five machine learning regression models – namely, artificial neural network, support vector machine, gradient boosting machine, random forest and k-nearest neighbors. ANN outperformed other models in predicting E. coli concentrations. In the data-driven analysis, the state of sand, processing temperature and the contact angle representing the wettability of the sand are identified as the most crucial parameters in predicting E. coli concentrations.