<p>Epileptic seizures represent the most widespread neurological disorder in the world. The efficient automatic detection of seizures is crucial for timely diagnosis and prompt treatment. In this paper, we propose a novel framework, IIRB-HybridFormer, that integrates a hybrid CNN transformer framework with time frequency representation for epileptic seizure detection from Electroencephalogram (EEG) signals. The proposed Inception-based Inverted Residual Bottleneck (IIRB) block utilizes a multi-branch feature extraction mechanism in conjunction with an inverted residual structure, ensuring efficient feature representation with minimal computational overhead. In the subsequent stage, Long Short-Term Memory (LSTM) is integrated with the Hybrid Transformer (HybridFormer) to eliminate the need for positional encoding, thereby reducing the computational complexity. The LSTM-derived feature vector is embedded in HybridFormer, which leverages Hybrid Pooled Multi-head Attention (HPMA) and an Inverted Residual Bottleneck-based Feed-forward Network (IRBFN) to enable robust local and global feature extraction. The algorithm’s effectiveness is evaluated on two publicly available EEG databases of varying sizes, namely the Bonn and CHB-MIT datasets. Experimental results demonstrate classification accuracy of 99.538% and 97.801% on the Bonn and CHB-MIT, respectively. The CHB-MIT dataset is also evaluated for event-based seizure detection, achieving a sensitivity of 100% and a false detection rate of 2.474/h. The proposed architecture exhibits a superior performance compared to the existing state-of-the-art models. It also surpasses other pre-trained models with its lightweight framework, along with reduced training and testing time. The results validate that the proposed IIRB-HybridFormer framework is robust and reliable in highlighting its strong potential for effective detection of epileptic seizures.</p>