In this project, we analysed the EEG signals (brain signals) of the patients suffering from Epilepsy (a neurological disorder). Based on the energy patterns, levels of intensity and unusual patterns which help identify a normal person from a person with seizures. We developed an ML classification model to predict the impending seizure attack beforehand and built an altering system in case of emergency. Benchmarked the model against various state of art classifiers and the promising results were obtained from Random Forest Classifier. The model is able to predict with an accuracy of 99%.