The joint usage of sub-6 GHz and millimeter wave (mmWave) bands is believed to satisfy the high data rate, low latency, and ultrahigh reliability requirements of the vehicle-to-everything (V2X) communication; thus, its channel modeling has advanced to the state of the art. This paper offers a comprehensive review of existing multiband based V2X communication channel modeling techniques and their associated challenges. To provide an overview of V2X communication, the system layout describing the vehicle to vehicle/infrastructure/pedestrian (V2V/V2I/V2P) links and performance goals is presented. Then, trend of large-scale and small-scale propagation parameters acquisition using deterministic and stochastic channel modeling approaches is described. The received power for various scatterers size is also examined to simulate the real-world scenarios in which a group of scatterers surround a car and observed that size of blockers impacts the receiving power. The 3D non-stationarity issue and lack of a flexible algorithm that can transition between sub-6GHz, mmWave, or combined modes based on the links' traffic inquiry category are challenges that have not yet been overcome. On the other hand, developments in mobile edge computing, integrated signal processing, channel modeling tools, and measurement-driven findings are presented as opportunities to further advance V2X communication. The role of potent technologies in advancing vehicular communication goals by their synergistic relation with mmWave frequency is also illustrated. Specifically, developing flexible algorithm and loading into edge computing enabled roadside units aids to meet dynamic resource scheduling targets which in turn triggers the realistic intelligent transport system. The hybrid sub-6/mmWave mode selection pseudo code is provided as well in this study as an insight to algorithm developers. Therefore, this article serves as a valuable reference for academics who are interested in this field providing an in-depth review of the joint sub-6GHz/mmWave based V2X channel modeling techniques for autonomous driving.