Unmanned Aerial Vehicles (UAVs)Unmanned Aerial Vehicles (UAVs) have emerged as a potential solution for beyond 5G networks due to their flexibility and cost-effectiveness. Recently, UAVsUnmanned Aerial Vehicles (UAVs) have grabbed the attention of researchers due to their wide range of uses, including airborne inspection, drone photography, crop monitoring, delivery services, and wireless communication. In wireless communications, using UAVsUnmanned Aerial Vehicles (UAVs) in conjunction with ground stations can improve coverage, dependability, and energy efficiency of the network or they can also be deployed as the flying mobile terminals within cellular networks. However, UAVsUnmanned Aerial Vehicles (UAVs) face several deployment challenges such as optimal placement and height during operation. Another emerging technology, reconfigurable intelligent surface (RIS)Reconfigurable Intelligent Surface (RIS), is used along with UAVsUnmanned Aerial Vehicles (UAVs) to further enhance the network efficiency. These low-cost and passive RISsReconfigurable Intelligent Surface (RIS) are regarded as the cost-effective alternative for networks beyond 5G and 6G. To broaden their application, they can be deployed on any structure, rooftop, or even UAVsUnmanned Aerial Vehicles (UAVs). The RIS-aided UAV networking design has a lot of potential for improving wireless communications and dealing with the increasing complexity of the wireless channel, especially at higher frequencies. The appropriate positioning and elevation of the UAV-RIS are critical for efficient downlink connection between the base station and edge users. Improving the minimum rates of edge users is also an essential factor to consider while implementing this infrastructure. In this chapter, our primary aim is to enhance the overall system rates in an RIS-enhanced UAV network through strategic collaboration with an RISReconfigurable Intelligent Surface (RIS). The key focus lies in the joint optimization of both UAV altitudes and passive RISReconfigurable Intelligent Surface (RIS) elements. To achieve this, we introduce a novel optimization approach, combining Riemannian Gradient and particle swarm Optimization (RGO) techniques. Addressing the inherent complexity of the problem, we adopt an alternating optimization strategy, breaking it down into two interconnected sub-problems. Our experimental results validate the efficiency and superiority of the proposed scheme when compared to baseline strategies.