In this work, we present a Deep Learning framework to predict the progenitor star’s characteristics of Supernovae (SNe) from their observed light curves. This task is crucial for astrophysics, as it can provide insights into the evolution of the star before the explosion and into the SN explosion mechanism. In fact, there is no direct mapping between the observed light curves and the progenitor’s characteristics, and the common techniques used to infer them are indirect, meaning that they rely on the comparison between the observed light curves and the light curves generated with some physical model simulation using the supposed progenitor’s characteristics as input. However, the physical models used to generate the light curves are not perfect, being either computationally expensive or based on simplifying assumptions. Here, we train a machine learning model on a dataset of light curves generated with a semi-analytical model — which is computationally efficient and accurate for the problem at hand — to predict the progenitor’s mass, radius, energy, and nickel mass from the observed light curves of SNe similar to SN 1987A. Our results show that the Deep Learning model effectively learns the complex mapping between the observed light curves and the progenitor’s characteristics with a low mean absolute percentage error, and we note that the proposed framework is general and can be applied to other types of SNe without significant modifications.