  Unmanned Aerial Vehicles (UAVs), as a recently emerging technology, enabled a
new breed of unprecedented applications in different domains. This technology's
ongoing trend is departing from large remotely-controlled drones to networks of
small autonomous drones to collectively complete intricate tasks time and
cost-effectively. An important challenge is developing efficient sensing,
communication, and control algorithms that can accommodate the requirements of
highly dynamic UAV networks with heterogeneous mobility levels. Recently, the
use of Artificial Intelligence (AI) in learning-based networking has gained
momentum to harness the learning power of cognizant nodes to make more
intelligent networking decisions. An important example of this trend is
developing learning-powered routing protocols, where machine learning methods
are used to model and predict topology evolution, channel status, traffic
mobility, and environmental factors for enhanced routing.
  This paper reviews AI-enabled routing protocols designed primarily for aerial
networks, with an emphasis on accommodating highly-dynamic network topology. To
this end, we review the basics of UAV technology, different approaches to swarm
formation, and commonly-used mobility models, along with their impact on
networking paradigms. We proceed with reviewing conventional and AI-enabled
routing protocols, including topology-predictive and self-adaptive
learning-based routing algorithms. We also discuss tools, simulation
environments, remote experimentation platforms, and public datasets that can be
used for developing and testing AI-enabled networking protocols for UAV
networks. We conclude by presenting future trends, and the remaining challenges
in AI-based UAV Networking, for different aspects of routing, connectivity,
topology control, security and privacy, energy efficiency, and spectrum
sharing.
