The onset of epileptic seizures is attributed to irregularities in the brain, which can have an impact on a person’s overall well-being. Seizures come on suddenly, with no warning signs, thereby elevating the risk of fatality. Put simply, a seizure arises when a large number of brain cells become stimulated simultaneously. Nearly 1% of people worldwide experience epileptic seizures. The predictionPrediction of seizures indeed remains a complex challenge. While advancements in technology and research have improved our understanding, the limited amount of data available can hinder progress. There are various methods employed to predict seizures, such as techniques based on frequency, statistical assessment of EEG signals, non-linear dynamics involving chaos, and the use of intelligent engineered systems. Forecasting seizures prior to their onset is advantageous in terms of preventing seizures through medication. Contemporary computational tools, along with machine learningMachine learning and deep learningDeep learning methods, are presently being employed to anticipate seizures utilizing EEG. The research in this paper entailed a comparative analysis of diverse machine learning algorithmsMachine learning algorithms employed to train a model specifically designed for predicting episodes of seizures.