Clinical decision support systems could be transformed by accurate forecasting of high-risk clinical signs, such as epileptic seizures, and new therapeutic approaches for patients. Physiological signals can be continuously recorded as a result to the advancement of pervasive sensor technology, preventing the catastrophic effects of epilepsy. However, the lack of an autonomous early warning system has hampered the development of seizure prediction. The most recent set of research groups electroencephalograph (EEG) clips and separates the clips that represent the commencement of epilepsy. If such seizures were predicted in advance then patients can overcome the injuries and effects, so can lead a smoother life. In spite of years of research, prediction of seizures is challenging. Therefore new advancements have been made in ML and DL based algorithms to predict the seizure using scalp EEG (sEEG).