Unmanned Aerial Vehicles, or UAVs, also known as drones, have drastically disrupted the aviation industry. The more the disruption, the more probable it is that legislation and technology will continue to advance. One such technology that could amplify this disruption is the UAV swarm. A UAV swarm is capable of assigning tasks to numerous UAVs while requiring no human interaction. Communication is vital for the management and coordination of a UAV swarm. In general, extra sensory or supplementary equipment is also required to deal with any issue with the swarm, like high-end essentially trustworthy communication to meet these challenges, including coordination and collaboration, control, security, mission planning algorithms, and more. Drone swarms benefit from the inclusion of Distributed Learning (DL) algorithms to endow them with target tracking, remote surveillance, power management, user association, perception, trajectory planning, wireless resource allocation, and communication services. This chapter presents a review of UAV swarms in IoT networks, the application of machine learning methods for swarm communication in UAVs and challenges in communication of swarm drones alongside the optimization techniques. Finally, we review their applications in UAV cluster job scheduling, federated reinforcement learning (RL) empowered aerial access networks (AANs) with low-earth orbit (LEO) satellites and UAVs, and energy-efficient wireless communications with distributed reconfigurable intelligent surfaces.