Despite the significant advancements in modern medicine, the prevalence rate of epilepsy remains high, affecting approximately 50 million people globally. When epilepsy is identified early, its management can significantly reduce the risk of long-term damage and promote improved living standards. Non-invasive techniques such as electroencephalography (EEG) are routinely used to diagnose this neurological condition by monitoring electrical activities in the brain. However, EEG data’s usage for research and diagnosis purposes creates numerous challenges related to patient privacy and safety measures of such sensitive digital information. This paper introduces a blockchain-enhanced platform fused with machine learning (ML) models that upholds confidentiality when analyzing EEG data as an ideal way to safeguard patient rights and enhance early epilepsy detection procedures. The study proposes a novel solution to secure private EEG data sharing using blockchain technology, discrete wavelet transform (DWT), and ML models that enhance the accuracy and reliability of early epilepsy diagnosis. When creating an ML model for analyzing EEG data related to epilepsy, privacy is one of the top priorities. To prevent unauthorized access, we turned to a blockchain solution that allowed us to secure both the storage and sharing mechanisms used when dealing with these sensitive patient files. This cutting-edge technology was designed with smart contracts, giving patients complete control over who would have permission to view their medical histories without compromising security and confidentiality. After evaluating actual EEG datasets available in real-world settings, it became clear that utilizing our blockchain-enabled approach yielded superior results compared to more traditional methodologies. This proposed approach consists of InterPlanetary File System (IPFS) and smart contracts that give complete control over permissions to access medical records without compromising data security. As per our research findings, blockchain technology has substantial potential to address privacy and security concerns in EEG data study and sharing for epilepsy diagnosis. Our work contributes to the growing literature on the intersection of ML, blockchain and healthcare. It has significant implications for developing more secure and privacy-preserving medical data-sharing systems in futuristic medical care.