Biodiesel is becoming a promising fuel in many markets in the world for being a renewable energy source and for not requiring significant adaptation in existing diesel engines. It is biodegradable and its polluting gas emissions are less harmful to the environment. Transesterification is used for the biodiesel synthesis at supercritical conditions using triacylglycerols and solvents in a heterogeneous reaction. The Peng-Robinson (PR), the volume-translated Peng-Robinson (VT-PR), and the perturbed chain statistical associating fluid theory (PC-SAFT) equations of state were using to predict the fluid phase behavior of systems containing solvents and components present in the synthesis of biodiesel at supercritical conditions. Two pure component parameters for the VT-PR equation, N and k(3), and the five pure component parameters for the PC-SAFT equation, m, sigma, and epsilon as well as the associating parameters kappa(A,B,) and epsilon(A,B,), were predicted based on the vapor pressures and the saturated liquid volumes. Results were compared with experimental data presented in the literature, considered thermodynamically consistent, and it was confirmed that noncubic equations of state are more accurate than cubic equations of state. Thermodynamic modeling was also compared with the thermodynamic simulation using artificial neural networks (ANN) and molecular descriptors at different architectures. Results, in terms of deviations of bubble pressures of and vapor phase composition, predicted by the optimum ANN model are slightly more efficient than the ones obtained by the thermodynamic models, mainly the PC-SAFT equation of state.