Epileptic seizure detection from electroencephalogram (EEG) signals represents a fundamental challenge in computational neuroscience, with traditional approaches limited by their inability to capture complex topological transformations in brain connectivity during ictal events. While topological data analysis has demonstrated promise for EEG analysis, existing methodologies primarily employ persistent homology features with conventional classifiers, failing to leverage the geometric structure inherent in neuralcomputation. To the best of our knowledge, this is the first work that applies topological neural networks—message passing architectures on simplicial complexes—to EEG seizure detection, integrating persistent homology features across multiple distance functions with temporal modeling, building upon Hajij et al.’s foundational work on topological deep learning architectures. The proposed approach introduces a novel 3-layer TNN framework that integrates multi-scale persistent homology with theoretically grounded topological message passing mechanisms. This research establishes mathematical foundationsfor seizure detection through topological invariants and provides convergence guarantees for the neural architecture. The model constructs four complementary distance matrices (correlation, Euclidean, phaselag, and coherence-based) from multi-channel EEG recordings, applying Vietoris-Rips filtrations to extract multi-dimensional topological features across scales. The core innovation lies in the rigorous implementation of the four-step topological message passing framework: message computation, within-neighborhoodaggregation, between-neighborhood aggregation, and feature update, combined with bidirectional LSTMnetworks for temporal modeling. Evaluation on the CHB-MIT dataset across 10 patients using event-based metrics demonstrates an F1-score of 74.36%, establishing the first successful integration of topological neural architectures with neurological signal processing. Theoretical analysis reveals that seizure events exhibit characteristic changes in topological entropy and Betti numbers, providing interpretable biomarkers for clinical translation.