  A galaxy cluster as the most massive gravitationally-bound object in the
Universe, is dominated by Dark Matter, which unfortunately can only be
investigated through its interaction with the luminous baryons with some
simplified assumptions that introduce an un-preferred bias. In this work, we,
{\it for the first time}, propose a deep learning method based on the U-Net
architecture, to directly infer the projected total mass density map from
idealised observations of simulated galaxy clusters at multi-wavelengths. The
model is trained with a large dataset of simulated images from clusters of {\sc
The Three Hundred Project}. Although Machine Learning (ML) models do not depend
on the assumptions of the dynamics of the intra-cluster medium, our whole
method relies on the choice of the physics implemented in the hydrodynamic
simulations, which is a limitation of the method. Through different metrics to
assess the fidelity of the inferred density map, we show that the predicted
total mass distribution is in very good agreement with the true simulated
cluster. Therefore, it is not surprising to see the integrated halo mass is
almost unbiased, around 1 per cent for the best result from multiview, and the
scatter is also very small, basically within 3 per cent. This result suggests
that this ML method provides an alternative and more accessible approach to
reconstructing the overall matter distribution in galaxy clusters, which can
complement the lensing method.
