The newly developed generalized NRTL-QSPR activity coefficient model constitutes a significant improvement over our previous generalization of the NRTL model. Specifically, an internally consistent generalization is provided for the NRTL interaction parameters using a more extensive database involving 578 binary systems. A non-linear QSPR model was developed for the NRTL parameters, where evolutionary algorithms combined with artificial neural networks were used to perform molecular descriptor reduction. The model predicts pressure and temperature of a binary VLE system within 6 % and 0.6 % average absolute deviation (AAD), respectively. Further, the generalized NRTL phase behavior predictions show a significant improvement over to the group contribution method, Universal Functional Activity Coefficient model (UNIFAC), which resulted in 9 % AAD for pressure predictions.