Physics-Informed Neural Networks (PINNs) are emerging as an increasingly promising area of research within the scientific community. This growing interest is attributable to their unique ability to integrate fundamental laws of physics into Deep Learning (DL) processes. The present research aims to explore the effectiveness of PINNs in addressing the complexities involved in bitumen aging prediction in order to introduce more efficient and accurate modelling methodologies. In this research project, a PINN was built in the Python programming language following a multi-physics approach, which allowed simultaneous consideration of the three main chemical -physical phenomena governing bitumen aging: heat diffusion, oxygen diffusion, and oxidation kinetic diffusion.