In this study, we systematically screened and evaluated thirteen machine learning platforms to identify the most optimal means of predicting DFT-calculated 19 F NMR chemical shifts. As in most end-case applications, not all machine learning models perform equally well under the constraints provided. From a dataset of several hundred fluorinated chemical structures, our highest performing model, GBR, achieved an R 2 of 0.93 and MAE of 2.89 ppm -3.73 ppm, with a runtime (for 3 Å) of 0.34 seconds. As suggested by the results of this model in addition to the other 12 evaluated, a radius of 3 Å is the optimal distance around each fluorine atom of the compound structure from which to extract neighboring atoms as model input features. This allows for consistent high performances and an effective bias-variance tradeoff.