The computational prediction of NMR chemical shifts using quantum mechanical calculations is now commonplace in aiding organic structural assignment since spectra can be computed for several candidate structures and then compared with experimental values to find the best possible match. However, the computational demands of calculating multiple structural- and stereo-isomers, each of which may typically exist as an ensemble of rapidly-interconverting conformations calculations, are expensive. In this work, we address both of these shortcomings by developing a rapid machine learning (ML) protocol to predict 1H and 13C chemical shifts through an efficient graph neural network (GNN) using 3D structures as input. Transfer learning with experimental data is used to improve the final prediction accuracy of a model training using QM calculations. When tested on the CHESHIRE dataset, the 13C chemical shifts are predicted with comparable accuracy to the best-performing DFT functionals (1.5 ppm) in around 1/6000 of the CPU time.