The Open Shop Scheduling Problem has been widely studied in the field of operations research for several decades. The work carried out in this area consists mostly of heuristic methods tailored to specific problems. Very little research have taken into consideration recent advances in machine learning (ML) when improving exact methods for these problems. Our approach consists in using the data resulting from solving a set of randomly generated problem instances to train a neural network that predicts new arcs of a “good” disjunctive graph on the branch and bound. The predictions are used to derive a new branching strategy.