Epilepsy is a long-standing illness defined by short episodes of aberrant brain activity caused by abrupt cell discharges. The illness is not communicable and might linger for a long period. Epilepsy affects roughly 50 million individuals worldwide, making it a prevalent neurological illness. Epilepsy monitoring is the most significant element of epilepsy diagnosis and also plays an important role in diagnosing the origin of epilepsy, assessing prognosis, and directing therapy. This paper details the principles and basic algorithmic models of commonly used neuroimaging techniques and describes the role of different monitoring techniques in the diagnosis and treatment of epilepsy. The paper compares the advantages and disadvantages of different monitoring techniques in their application and explores a comprehensive and less restrictive epilepsy monitoring protocol for readers and relevant researchers. Currently, electroencephalography (EEG) is the most common technique for monitoring epilepsy, and its most basic algorithmic models are independent component analysis (ICA) and discrete wavelet analysis (DWA), which are used for aspects such as noise removal and feature extraction. This article is dedicated to helping the reader or relevant researcher to gain a more comprehensive and systematic understanding of current neuroimaging techniques and medical devices. Furthermore, it seeks to forecast future research directions based on current difficulties in the area. The purpose of this study is to give a useful reference for future research in the field of epilepsy monitoring.