Modeling the phase behavior of substances using thermodynamic equations of state (EOS) has been a very vital area of research, as it is a quick and inexpensive way of predicting the phase properties at desired conditions. The parameters obtained after parameterising the pure components are used along with appropriate mixing rules to predict the mixture phase behavior of these compounds. Prior optimization techniques used for parameterization of pure components are methods that converge at a local minimum. In a previous work, it was found that number of local minima varies depending on wide variety of factors, such as the objective function used, number of data points, the weighting of the data points. The local minimum found and ultimately used to predict mixture properties have a very large impact on the quality of the resulting predictions. However, recent advances in global optimization will enable us in finding all the local minima. This research focuses on studying the predictive power of a complex thermodynamic equation of state (here, SAFT-VR EOS) that will, in turn, improve one's ability to model these complex systems in the future. This work attempts to characterize and maximize predictive power by mitigating spurious conclusions that are based on results from local minimization schemes and, thus, allow for more definitive conclusions on the properties of systems in the absence of experimental data. To this end, this work employs global terrain methodology – an advanced global optimization technique and applies it to the field of thermodynamic modeling. Additionally, analyses on the effects of multiple parameter-sets and binary interaction parameters on the prediction of mixture properties will be discussed. Through this work, parameter rules will be developed that will allow for the optimal prediction of pure component properties.