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Fuel spray and atomization processes affect the combustion and emissions characteristics of fuels in internal combustion engines. Biodiesel and synthetic fuels such as oxymethylene dimethyl ethers (OME) show great promise as alternative fuels and are complementary in terms of reproducing the fluid properties of conventional diesel fuels through blending, for instance. Averaged experimental results, empirical correlations and Computational Fluid Dynamics (CFD) have typically been used to evaluate and predict fuel spray liquid and vapor penetration values so as to better design internal combustion engines. Lately, Machine Learning (ML) is being applied to these investigations. Typically, ML spray studies use averaged experimental data and then over-trained neural networks on the limited available data.