We present a hybrid surf-zone model that combines numerical simulations and statistical/machine learning techniques, enabling accurate calculations of nearshore wave and hydrodynamic parameters with high computational efficiency. The approach involves defining representative forcing conditions, carrying out numerical model (XBeach) simulations for these cases, and using the results to train machine learning models capable of predicting selected model output variables. Data decomposition via Empirical Orthogonal Function analysis further simplifies the process, reducing the output data dimensionality, with minimal accuracy loss (with exception of certain wetting-drying processes). Three machine learning approaches of increasing complexity are compared: a multi-variate linear regression (LR), a Radial Basis Functions (RBF) interpolator and a Deep Neural Network (DNN). The LR model fails to account for the complex non-linearities in coastal wave dynamics, which warrants the use of more complex machine learning techniques. Both the RBF interpolator and the DNN models demonstrate high levels of accuracy in the prediction of standard wave parameters, including short and long (infragravity) wave heights, mean wavelength, fraction of breaking waves, and depth-averaged currents. The proposed surrogate model thus offers an efficient alternative to computationally expensive numerical model simulations, enabling rapid and reliable calculations of climatologies of nearshore hydrodynamic conditions and modelling of specific event scenarios. We provide a comprehensive description of the implementation details and assess the surrogate model’s performance in representing various wave and hydrodynamic parameters. We discuss potential use cases and limitations, noting that this hybrid modelling technique can be adapted for use with other numerical models in various settings.