The widespread use of the Internet and the increase in the number and variety of devices connected to the internet have led to the emergence of new methods in wireless communication. Dynamic and temporary Ad-Hoc networks, which do not require a fixed infrastructure as in traditional wireless network communication, are one of these new methods. The fact that Ad-Hoc networks do not need a fixed infrastructure has revealed a network structure with a lower cost and less configuration. Mobile Ad-Hoc networks play an important role, especially in the communication of nodes on the move. FANET (Flying Ad-Hoc Networks) networks, which are called flying ad hoc networks, are mobile Ad-Hoc networks used for communication of unmanned aerial vehicles (UAV), and VANET (Vehicular Ad-Hoc Networks) networks, which are called vehicular ad hoc networks, are mobile Ad-Hoc networks used for communication of road vehicles. The development and dissemination of these networks make a significant contribution to the development of autonomous vehicles and UAVs. The increase in the use of FANET and VANET networks, which are specialized subnets of mobile Ad-Hoc networks, and the increase in the number of nodes in these networks have caused problems related to security, efficiency, and sustainability in these networks. Machine learning methods, one of today' s effective and common approaches, are one of the ways that are frequently used in solving the problems specified in FANET and VANET networks. The rapid topology change, which is one of the most important features of these networks, makes it difficult to provide traffic management, trust management, routing, and data transmission. In this direction, machine learning approaches play an active role. In this study, it is presented by examining which machine learning techniques are used in the literature to perform important tasks such as traffic management, trust management, routing, and data transfer. Thus, it is aimed for those who will work in these fields to acquire information about machine learning approaches that can be used. Since the FANET network type is a new approach, it has been observed that there are few studies using machine learning. In VANET systems, studies using machine learning methods are especially intense in 2021. This study was carried out to give the reader an idea about which machine learning methods can be used in which problems in FANET and VANET networks.
