We assess the ability of machine learning emulators, from Multivariate Linear Regression to Long Short-Term Memory (LSTM) networks, to reproduce storm surge dynamics in the northern Adriatic Sea. Using the corrected Mean Absolute Deviation squared (MADc²) loss function, we demonstrate that data-driven models can match high-resolution hydrodynamic simulations in representing extreme surge events with greatly reduced computational cost.