Wireless networks are key enablers of ubiquitous communication. With the
evolution of networking technologies and the need for these to inter-operate
and dynamically adapt to user requirements, intelligent networks are the need
of the hour. Use of machine learning techniques allows these networks to adapt
to changing environments and enables them to make decisions while continuing to
learn about their environment. In this paper, we survey the various problems of
wireless networks that have been solved using machine-learning based prediction
techniques and identify additional problems to which prediction can be applied.
We also look at the gaps in the research done in this area till date.