GaMPEN (Galaxy Morphology Posterior Estimation Network) estimates robust posteriors (i.e., values + uncertainties) for structural parameters of galaxies using a Bayesian machine learning framework. The code also automatically crops input images to an optimal size before structural parameter estimation. The package produces extremely well-calibrated (less than 5% deviation) predicted posteriors; these have been shown to be up to 60% more accurate compared to the uncertainties predicted by many light-profile fitting algorithms. Once trained, it takes GaMPEN less than a millisecond to perform a single model evaluation on a CPU. Thus, GaMPEN’s posterior prediction capabilities are ready for large galaxy samples expected from upcoming large imaging surveys, such as Rubin-LSST, Euclid, and NGRST.