Abstract—In the modern era of widespread wireless devices and their underlying connectivity requirements in the Internet of Things, efficient use of resources across networks is inevitable. The 5G emerging techniques aim to equip each application with advanced features and to make connectivity between the devices ubiquitous. Therefore, the Resource Allocation (RA) in the 5G and 6G networks has gained more research traction to use the sparse resources in the wireless channels effectively. In Vehicular communication encompassing autonomous driving, there is a need to embed the Multi-Radio Access Technology (Multi-RAT) to form Heterogeneous Networks (HetNet) and meet the diverse network requirements. Thus, this paper provides an overview of the resource allocation enablers and methods for Beyond 5G (B5G) Vehicle-to-Everything (V2X) systems. It begins by discussing the key features of B5G V2X systems, such as the slicing RA technique. The paper then proposes and describes a taxonomy that represents the enablers of RA in B5G V2X systems in the network and physical layers. The paper also discusses various allocation methods used as keys of 6G networks, such as machine learning algorithms, deep learning algorithms, and management modelling, such as graph and game theories. At the end of this paper, some open research challenges are discussed, such as the efficient use of machine learning algorithms in RA and virtual RA backhauling.