Therefore, it is obvious that each method--either conventional optimization theory or ML counterpart has certain pros and cons, and it is not straightforward to point to one method as superior to another; instead, they should be employed according to the conditions and requirements. Apart from the optimization method being employed, there are also various and distinct perspectives to be focused on in terms of energy optimization for UAV-assisted wireless networking. We can observe some well-studied concepts, including (1) positioning where the optimum three-dimensional positions of UAV-BSs are determined [23; 24]; (2) trajectory planning where the paths of UAV-BSs are designed [25; 26]; (3) resource management [27; 28]; (4) flight and transmission scheduling [29; 30]. In addition to these techniques, there is also a novel technique that has recently attracted considerable attention, namely, landing spot optimization, where UAV land on designated platforms to minimize propulsion energy due to flying or hovering, which is the most significant source of energy consumption [31; 32]. In this survey, we first provide an overview of UAVs with top-level taxonomy in terms of their wing types (i.e., fixed and rotary) followed by an in-depth analysis of the power supply and charging mechanisms of UAVs. Considering the discussion on EE so far, the power supply and charging system are at the heart of the UAV-assisted wireless networking. Then, we thoroughly investigate the role of UAVs in wireless communication networks by presenting various use-cases, including backhauling, load balancing, capacity enhancement, etc. With this discussion, we aim to reveal how broad the application range of UAVs in wireless communications is and that they will be at the core of 5G and 6G networks. The types of UAV-BS deployments are also reviewed in this survey to distinguish between standalone UAV deployment and UAV-BS deployment with fixed BSs. In the former, there is no existing cellular network infrastructure available, while in the latter, UAV-BSs assist the existing cellular network. We then investigate the energy optimization phenomena in UAV-assisted wireless networking to showcase different types of energies to optimize, such as propulsion energy and communication energy. This discussion is important to understand the main energy consumers in a UAV-BS, which specifies the energy optimization objectives accordingly. Figure 1: A top-level summary of energy optimization perspectives in UAV-based communication systems and corresponding approaches. As we mentioned earlier, there is a mountain of diverse optimization techniques available in the literature, and thus, these techniques are surveyed here under the top-level taxonomy of conventional techniques (e.g., heuristics) and ML techniques. The idea here is to capture general frameworks of those algorithms to better understand the state of the art in the literature. Then, energy optimization techniques--as the primary focus of this survey--are extensively surveyed under three-layer categorization: (1) deployment type; (2) energy optimization techniques (e.g., positioning, trajectory planning, etc. ); and (3) the optimization method (e.g., conventional and ML). Under the second layer (e.g., the optimization techniques), we included the above-mentioned landing spot optimization concept. It is better to highlight this because it is quite novel and one of the most important contributions of this survey since, to the best of our knowledge, the landing spot concepts has never been covered in any survey work so far. The three-layer categorization is performed in order to provide a better understanding with such a detailed mapping. After that, we also discuss some enabling technologies for energy-efficient UAV-assisted wireless networking. In that, we cover reconfigurable intelligent surfaces (RIS), mobile edge and cloud computing, network slicing, and cooperative communications, which are quite timely and promising technologies.