Estimating parameters in nonlinear thermodynamic models pertaining to vapor-liquid equilibrium (VLE) is of considerable importance and curiosity in the chemical processes. The parameter estimation for VLE models may have several challenges and hitches, including non-differentiable model equations, convergence problems near local minima, flat objective functions close to the global optimum, and ill scaled model equations. In 2023, the authors proposed an opposite point-based differential evolution (OPDE) algorithm which is an enhanced form of classical DE algorithm. In this work, the application of OPDE algorithm to VLE data modeling has been demonstrated. The parameters in different thermodynamic models are estimated using several VLE datasets for systems at various temperatures and pressures. This study also compares the performance of the OPDE algorithm with DE algorithm. The OPDE algorithm successfully estimated model parameters with a very high success rate. The OPDE algorithm outperformed DE algorithm. Also, new results obtained from some of the VLE data modeling problems are presented.