Nowadays epileptic seizure detection and classification has been an important concern as the central nervous system is affected in epilepsy due to brain disorders that cause changes in neural activity. Seizures result from unexpected brain activity, excitement, and sometimes unconsciousness. Epilepsy is a profound condition, with approximately 1% of people worldwide affected by it, and 10% reporting that they deal with this issue daily. It has the potential to impact life prospects significantly. The primary purpose of an EEG report is to record the electrical brain activity generated within the brain. Using EEG, doctors and scientists can analyze brain activity and identify abnormalities. Several research studies using machine learning and deep learning techniques have been successful in recognizing EEG patterns associated with epileptic seizures. The proposed study explains and compares various existing algorithms and methods for detecting and classifying epileptic seizures based on certain parameters. The analytical results indicate that the KNN, SVM, and CNN classifiers are most preferred, and the Bonn dataset and CHB-MIT datasets are the most used choices.