The application for machine learning was seen to be beneficial in the field of medicine, especially with the way current systems process electroencephalogram data (EEG) to detect and recognize seizures. This work is aimed at developing a simpler, cost-effective but equally accurate system of recognizing and visualizing epileptic seizures based on electroencephalogram data (EEG). Utilizing public EEG datasets, the study used current processes known in the field of data-science such as pre-processing the data to remove discrepancies, combined with the powerful integrated programming environment Python and external machine learning packages like Brian, the study created a Binary-classification neuron model, utilizing an leaky-integrate-and-fire model combined with an unsupervised learning algorithm called spike-time dependent plasticity (STDP) to provide the most accurate results during testing. The results exhibited a high sensitivity of the created neuron model, and it obtained an accuracy score of 94.6%. The proposed spiking neural network model has been found to be exceptionally efficient in recognizing and visualizing epileptic seizures in a binary classification example; however, multi-classification problems such as analyzing EEG data by multiple classifications will require a more complex SNN Model to be developed.