In the post-COVID-19 era, the demand for polypropylene (PP) has been growing for uses for medical and food packaging applications, resulting in the generation of a huge amount of plastic waste and its accumulation in the environment. This project aimed to develop a mathematical model that has predictive power for the pyrolysis process of PP. To overcome the limitations of existing kinetic data, we developed a six-lump model consisting of plastic molecules, melted plastics, heavy fuel oil, light fuel oil, non-condensable gas, and char to describe the pyrolysis of PP. The six-lump model consisted of ten monomolecular, irreversible, first-order reactions. The initial values of the Arrhenius constants and activation energies of the reactions were set based on the kinetic constants from the literature. Then, the kinetic parameters were calibrated with an experimental dataset of the pyrolysis of PP to give a <10% difference between the compositions of the major products from the simulation results and the experimental data. The model was used to predict an optimum temperature for the production of pyrolysis oil, mass balances, carbon conversion, and energy efficiency of an industrial-scale integrated pyrolysis-condensation-combustion process using Aspen Plus. The proportion of pyrolysis oil reached the highest value at 460 °C. At this condition, the proportions of pyrolysis oil, char, and gas were 59.17%, 13.18%, and 27.65%, respectively, with carbon conversion and energy efficiency of 87.30%, and 87.56%, respectively. The simulation results predicted a higher yield of gasoline (0.3595 kg/kg of PP) than heavy diesel (0.2675 kg/kg of PP).