Thrombus formation in blood vessels is a medical problem which manifests itself both spontaneously and after the insertion of medical devices inside human bodies. Around 500000 people die in the EU each year because of it, it is thus crucial to develop robust computational methods to predict its formation. This work shows how Deep Learning and Model Order Reduction techniques can be applied in order to enhance physical simulations of blood clots formation. A few simulations were carried out with the software OpenFoam, to solve the Navier-Stokes equations for the blood flow and a set of parametrized PDEs, which couple the concentrations of the biochemical species with the velocity field of blood.