In this article, we suggest a sophisticated approach to identify epileptic episodes using EEG data. The first step in the procedure is the capture of unprocessed EEG signals using specialist apparatus. To ensure that only pertinent frequencies are kept, these signals are filtered to eliminate artifacts and noise. The signals are standardized in order to preserve consistency throughout the dataset. The EEG data is then broken down into intrinsic mode functions using the Hilbert-Huang Transform, which extracts instantaneous frequencies and efficiently captures nonlinear and nonstationary features. Spectrograms are created from these altered signals and then gray scale to minimize dimensionality while maintaining important seizure-related characteristics. These gray scale spectrograms are used to train an autoencoder, which then uses the data to compress and recreate itself, finding patterns linked to seizure activity. In the end, a neural network classifier is used to differentiate between seizure and non-seizure events, and it achieves 95.21% training accuracy and 82.30% validation accuracy. The suggested approach shows great promise for precise and effective seizure identification, opening the door for better patient care and monitoring.