Modeling large-scale flood inundation requires weeks of calculations using complex fluid software. The state-of-the-art in operational hydraulic modeling does not currently allow flood real-time forecasting fields. Data driven models have small computational costs and fast computation times and may be useful to overcome this problem. In this paper, we propose a new modeling approach based on a coupled of Hydrodynamics finite element model and Multi-headed Deep convolutional neural network (MH-CNN) with rain precipitations as input to forecast rapidly the water depth reached in large floodplain with few hours-ahead. For this purpose, one first builds a database containing different simulations of the physical model according to several rain precipitation scenarios (historic and synthetic). The multi-headed convolutional neural network is then trained using the constructed database to predict water depths. The pre-trained model is applied successfully to simulate the real July 2014 flood inundation in an 870 km² area of La Nive watershed in the south west of France. Because rain precipitation forecast data is more accessible than discharge one, this approach offers great potential for real-time flood modelling for ungauged large-scale territories, which represent a large part of floodplain in the world.