In most of tests and models to evaluate the thermal cracking of asphalt mixture, the consideration of different elements within the asphalt mixture has been neglected to provide a corrective solution. Accordingly, in this research, the evaluation of the mechanisms of thermal cracking involved assessing the impact of surface free energy (SFE) parameters and other fundamental properties of bitumen and aggregate. These parameters included the adhesion and cohesion free energy, the ratio of the base element to the acid element of the aggregate SFE (RBA), the creep stiffness of bitumen, the rate of changes in creep stiffness (m-value), and specific surface area (SSA) of aggregate. To this end, Wilhelmy plate (WP) test, universal sorption device (USD) test, the bending beam rheometer (BBR) test and the pull-off test were performed. In the following, models were presented to evaluate the effect of mentioned parameters as independent variables on the mechanisms of thermal cracking using an artificial neural network (ANN). The sensitivity analysis of the Artificial Neural Network (ANN) model revealed that in the status of adhesive failure, the cohesion free energy and m-value did not affect the Pull-off adhesion strength. On the other hand, the adhesion free energy, RBA, and the SSA were directly proportional and the creep stiffness was inversely proportional to the Pull-off adhesion strength. Also, in the status of cohesive failure, the adhesion free energy, the RBA, and the SSA did not affect the Pull-off cohesion strength. On the other hand, the cohesion free energy and m-value were directly proportional and the creep stiffness was inversely proportional to the Pull-off cohesion strength.