A graph neural network enhances algebraic multigrid solvers by predicting optimal polynomial coefficients for sparse pseudo-inverse operators. The method reduces V-cycle iterations and achieves wall-clock speedups of 4% to 37% across benchmarks, with robust performance on meshes up to 128 times larger than training data and on unseen industry problems like AirfRANS.