Coastal protection is of paramount importance because erosion and flooding affect millions of people living along the coast and can largely influence countries’ economy. The implementation of nature-based solutions for coastal protection, such as sand engines, has become more popular due to these interventions’ adaptability to climate change. This study explores synergies between AI and hydro-morphodynamic models for the creation of efficient decision-making tools for the choice of optimal sand engines configurations. Specifically, we investigate the use of long-short-term memory (LSTM) models as predictive tools for the morphological evolution of sand engines. We developed different LSTM models to predict time series of bathymetric changes across the sand engine as well as the time-decline in the sand engine volume as a function of external forces and intervention size. Finally, a MATLAB framework was developed to return LSTM model results based on users’ inputs about sand engine size and external forcings.