An electroencephalogram (EEG) is a critical diagnostic tool that monitors brain activity and detects epileptic seizures. The EEG signals, however, are complex and unwieldy, making it challenging to create an automated seizure-detection system. Additionally, sensitive healthcare data in clinical systems should be handled securely. The given paper describes an EEG-based seizure detection using blockchain security and transformer graph capsule networks (PT-GG-CapsNet-Blockchain). This model integrates a superior signal processing system, machine learning, and blockchain to attain powerful seizure tracking and data protection. The structure takes EEG data of the CHB-MIT dataset, which consists of multichannel records of seizures in children. Preprocessing was performed using the Adjoint Bilateral Filter (ABF), which removes noise while preserving critical signal features. Using the Clifford Fourier Mellin Transform (CFAT), spatio-temporal features are extracted and thus capture the complex dynamics in EEG signals. Classification will be performed by a Pure Transformer-Based Gated Graph Attention Capsule Network (PT-GG-CapsNet), leveraging transformers' temporal analysis capabilities and graph capsule networks' modeling of spatial relationships. The Artificial Hummingbird Algorithm (AHA) will then be used for hyperparameter optimization, ensuring an optimal model. To achieve data security and transparency, the framework incorporates blockchain technology to allow decentralized storage of EEG data. This guarantees integrity, immutability, and effective coexistence among heterogeneous medical networks. The classification accuracy, precision, recall, and F1-score of the proposed model are 99.7%, 99.5%, 99.2%, and 99.3%%, respectively, whichbeats baseline methods. This framework will offer real-world clinical uses of blockchain that can operate in a heterogeneous medical setting, thanks to its low latency and interoperability.