This article proposes a novel approach to the identification of epileptic seizures from electroencephalogram (EEG) data obtained from both normal and epileptic people. EEG data contains valuable information about several physiological states of the brain. Our method makes use of hierarchical clustering algorithms to identify seizures. We evaluated the CNN + SVM, VGG16, DenseNet121, MobileNetV2, and Xception models for this task. Remarkably, DenseNet121 achieved perfect accuracy, with MobileNetV2 and VGG16 following closely after. Xception obtained high accuracy, but the CNN + SVM model obtained substantially less. Based on our research, we determined that DenseNet121 was the ideal model to utilize while creating our Flask front-end application. The frontend’s usage of DenseNet121 provides a solid foundation for real-time seizure identification, which may benefit both patients and medical professionals. Our findings highlight the efficacy of deep learning techniques, specifically DenseNet121, for accurate and reliable seizure identification from EEG data. This marks a significant advancement in the identification and management of neurological conditions.