Epilepsy is a chronic neurological disorder distinguished by many types of seizures, some of which are characterised by involuntary repeated convulsions that have a significant impact on the patients' everyday lives. Several strategies have been presented in the literature to detect these types of seizures and identify the patient; however, these tactics fall short in ergonomic difficulties and adequate integration with the exercise equipment. This research work , we developed a machine learning classification algorithm to determine if EEG data indicates a seizure is occurring. To put it another way, the top performing model outperforms random guessing by a factor of 4.3, or "lift." As for the positive classes in the test set, it predicts them with a 100% accuracy rate. It is reasonable to assume this level of performance if this model is deployed in production to predict whether a patient is having a seizure.