Inkjet additive manufacturing is the next step toward ubiquitous manufacturing by enabling multi-material printing that can exhibit various mechanical, electronic, and thermal properties. These characteristics are realized in the careful formulation of the inks and their functional materials, but there are many constraints that need to be satisfied to allow optimal jetting performance and build quality when used in an inkjet 3-D printer. Previous research has addressed the desirable rheology characteristics to enable stable drop formation and how the metallic nanoparticles affect the viscosity of inks. The contending goals of increasing nanoparticle-loading to improve material deposition rates while trying to maintain optimal flow dynamics is the closely held trade secret in formulating these inkjet compositions. We use data from previous experiments and the CRC Handbook of Chemistry and Physics to train machine learning regression models to predict the relevant factors of inkjet printability at a standardized temperature of 25ºC: viscosity, surface tension, and density. These models were used to predict the rheological factors of the main components of a UV-curable inkjet ink formulation: UV-curable monomers and oligomers, photoinitiators, dispersants, and humectants. This paper compares the relative performance of five machine learning algorithms to assess the effectiveness of each approach for chemoinformatics regression tasks.