The nonrandom two-liquid (NRTL) model is an activity coefficient model used widely in phase equilibria calculations. The NRTL model has three adjustable parameters that are determined through regression of experimental data for a specific system. A generalization for the model parameters would reduce the time, money and effort expended on the collection of experimental data. This work focuses on the application of a theory-framed quantitative structure-property relationship (QSPR) modeling approach for the estimation of NRTL parameters. A database of 342 low-temperature binary (10-40 degrees C) liquid-liquid equilibria (LLE) systems was employed in this work. Data regression analyses were performed to determine the NRTL model parameters. Structural descriptors of the molecules were generated and used in developing a QSPR model to estimate the regressed NRTL parameters. The newly developed QSPR model uses 30 significant descriptors as inputs. The model yielded binary predictions with 8, 38, 51 and 44% absolute average deviation for the mole fractions (x(1) in 1-rich phase and x(1) in 2-rich phase) and partition coefficients (K-1 and K-2), respectively. These errors are approximately 3 to 4 times the errors found from the regression analyses. Further, we observed an 11% prediction failure rate, in which cases the model fails to converge to an equilibrium solution. The application of the popular and often employed UNIFAC-1981-LLE model resulted in 3 to 7 times the errors obtained through regression analyses and a prediction failure rate of 36%. These results demonstrate the efficacy of our QSPR model in providing improved predictions and an increased range of applicability when compared to the UNIFAC-1981-LLE model for LLE systems.