Particulate emissions from gasoline direct injection (GDI) engines continue to be a topic of substantial research interest. Forthcoming regulation both in the USA and the EU will further reduce their emission and drive innovation. Substantial research effort is spent undertaking experiments to understand, characterize, and research particle number (PN) emissions from engines and vehicles. Recent advances in computing power, data storage, and understanding of artificial intelligence algorithms now mean that these are becoming an important tool in engine research. In this work artificial neural network (ANN) and random forest (RF) algorithms for the prediction of PN emissions from a highly boosted (up to 32 bar BMEP) GDI engine are used. Particle size, concentration and the accumulation mode geometric standard deviation (GSD) are all predicted by the two models. The ANN and RF results are compared, and an in depth study on parameter importance is carried out. The Random Forest algorithm is used as an estimator and the various engine parameters are ranked with a permutation feature importance technique using mean squared error as a performance metric. The results show that from 158 input parameters, only between 13 and 31 (depending on which output is of interest) are needed to characterize the concentration, size, and GSD of the particle spectrums to within 95% of the cumulative mean squared error. Overall, both models show good agreement to the experimental data.