The complexity of the analysis of electroencephalography (EEG) is a difficulty for application in clinical practice, it has historically relied on expert interpretation in an effort to identify discrete patterns of signals, and that makes scalability and efficiency difficult. In response to this challenge, a method of EEG signal automation classification using deep learning has been developed to help in the diagnosis of neurological disease through the EEG signal classification of six types: seizure, lateralized periodic discharges (LPD), generalized periodic discharges (GPD), lateralized rhythmic delta activity (LRDA), generalized rhythmic delta activity (GRDA), and other. This approach uses a multi-class EEG classifier based on ResNet blocks, 1D convolution, and GRU layers to learn EEG signal multi-scale and temporal features. The data used are EEG samples divided into training, validation, and test sets, and the architecture is optimized for processing sequences efficiently and hence ideal for pattern detection specific to every neurological disorder. Performance measures reflect the model’s strength, with an accuracy of 83.28%, a macro F1 measure of approximately 79.85%, and a Kullback-Leibler (KL) divergence loss of 0.26 during training. The high F1 score also reflects the model’s strength for clinical application, with the potential to simplify diagnostic procedures. The results reveal the strength of convolutional networks to classify EEG and enable large-scale automated neurological evaluation solutions of the future.