Riverine bathymetry plays an essential role in safe maritime transportation, prediction of land sinking, and flood risk management. Nevertheless, direct measurement of riverbed profile has been proven costly, making alternative approaches such as estimation of bathymetry through surface flow velocity measurement a favorable substitution. Here, we learn the functional relationship between riverine bathymetry and surface flow velocities using autoencoder, as a data-driven algorithm, in the presence of varying boundary conditions and compare our results with both model-based and data-driven approaches.