The growing production of plastic waste and improper dumping after use has become a worldwide challenge. This waste is a substantial source of petroleum and can be effectively converted into pyrolytic oil and other useful products. A statistical prediction of the rate constants is essential for optimizing pyrolysis process parameters, such as activation energy (Ea), frequency factor (Ao), temperature (T), and kinetic rate constants (k). In this research, we utilized Box−Behnken using RSM with Design Expert software to predict statistical rate constants at 500 °C and 550 °C. The efficiency of the predicted rate constants was investigated and compared to the findings of experimental rate constants extracted from the literature. At 500 °C, the estimated rate constants did not reveal a significant rise in the oil output since these constants promoted high gas yield. Compared to the experimental rate constants, statistically predicted rate constants at 550 °C demonstrated substantially high-oil output with only 1% byproducts. The experimental rate constants yielded 32% oil at 550 °C, whereas the predicted rate constants yielded 85% oil. The statistically predicted rate constants at 550 °C could be used to estimate commercial-scale extraction of liquid fuels from the pyrolysis of high-density plastics. It was also concluded that Ea, Ao, and T must be analyzed and optimized according to the reactor type to increase the efficiency of the expected rate constants.