In this paper we propose a method based on geometric deep learning to reduce the computational complexity of the Maximum Clique Enumeration (MCE) problem. Specifically, given a network, we employ geometric deep learning to extract a sub-network on which running the MCE algorithm. Our approach is based on finding a strategy to remove from the network nodes that are not functional to the solution, while keeping all maximum cliques. The resulting network will have a reduced size and, as a result, search times of the MCE is reduced. We show that our approach is able to obtain significant speed-ups while preserving all maximum cliques.