This paper proposes a novel deep learning architecture, EEG-TBSANet, for automatic detection of epileptic seizures from electroencephalogram (EEG) signals. The model integrates temporal convolutional networks (TCN), bidirectional long short-term memory (BiLSTM) networks, and self-attention (SA) mechanisms in one single framework which allows the mechanism to locally and globally extract short and long temporal features while adaptively focusing on relevant components of the signals to detect seizures. EEG-TBSANet was fully tested on three publicly available benchmark datasets: Guinea-Bissau, Bonn, and CHB-MIT, which have different characteristics of signal types and acquisition scenarios. The model achieved classification accuracies of 98.02%, 98.57%, and 99.40% for the corresponding datasets under competitive conditions with several significant baseline and ablation models, including TCN-SA, LSTM-GRU, CNN-BiLSTM, TCN-BiLSTM, MDFLN. Additionally, an extensive ablation study confirmed the critical role of each architectural component in enhancing detection performance. The results show the model’s strong generalization ability across datasets with varied clinical and technical conditions. These results demonstrate that EEG-TBSANet offers a robust and generalizable solution for EEG-based epileptic seizure detection.