Solving Constraint Optimization Problems (COP) requires exploring a large search space. By providing objective boundaries, this space can be pruned. Finding close boundaries, that correctly under- or overestimate the optimum, is difficult without having a heuristic function for the COP. We present a method for learning to estimate boundaries from problem instances using machine learning. The trained model can then estimate boundaries for unseen instances and thereby support the constraint solver through an additional boundary constraint.