This extensive literature review investigates the current state of electroencephalogram (EEG) signal detection for epileptic seizures, with a particular focus on the criticality of early detection and intervention in the management of epilepsy. The paper commences by presenting a contextual overview of the prevalence of epilepsy and emphasizes the vital significance of EEG signals in seizure detection. A comprehensive literature review is undertaken using a systematic approach, which includes a wide range of studies concerning the detection of seizures using EEG, the methodologies utilized, and the existing limitations in this field. The fundamental concepts underlying EEG signals are explained, differentiating characteristic features of normal and epileptic EEG. An exhaustive evaluation of the current methodologies employed in EEG-based seizure detection is conducted, encompassing time-domain and frequency-domain analyses as well as machine learning strategies. An exhaustive discussion is provided of the difficulties and constraints implicit in the analysis of EEG signals, including noise, variability, and ethical considerations. This article explores recent developments in the field, with a particular focus on hybrid approaches that incorporate diverse signal processing methodologies and deep learning techniques. This article examines the performance measures and evaluation metrics utilized in the benchmarking of epilepsy detection algorithms, emphasizing the need for standardized evaluation protocols. In summary, the paper provides a synopsis of significant discoveries and their ramifications for the progressive domain of epileptic seizure detection utilizing EEG.