  With the advancement in artificial intelligence (AI) and machine learning
(ML) techniques, researchers are striving towards employing these techniques
for advancing clinical practice. One of the key objectives in healthcare is the
early detection and prediction of disease to timely provide preventive
interventions. This is especially the case for epilepsy, which is characterized
by recurrent and unpredictable seizures. Patients can be relieved from the
adverse consequences of epileptic seizures if it could somehow be predicted in
advance. Despite decades of research, seizure prediction remains an unsolved
problem. This is likely to remain at least partly because of the inadequate
amount of data to resolve the problem. There have been exciting new
developments in ML-based algorithms that have the potential to deliver a
paradigm shift in the early and accurate prediction of epileptic seizures. Here
we provide a comprehensive review of state-of-the-art ML techniques in early
prediction of seizures using EEG signals. We will identify the gaps,
challenges, and pitfalls in the current research and recommend future
directions.
