<p>The constraints of current intelligent healthcare systems are extensively addressed in this study, which offers a thorough framework for expanding eHealth within the human-centered paradigm of Society 5.0. Reliance on centralised cloud systems, which have delay for real-time IoMT data, single points of failure, and serious data privacy/security threats, are some of the disadvantages of current approaches. Traditional healthcare AI models are often black-boxes, lacking explainability and transparency (XAI), which are necessary for patient acceptance and physician trust. To overcome these issues, the Proposed Approach uses a novel combination of specialised technologies and a multi-tiered architecture of cloud services, edge computing, and IoMT. The Health Prediction using Cloud Edge 2.0 (HPCE 2.0) algorithm employs fuzzy logic to combine static Electronic Health Records (EHRs) with dynamic real-time IoMT data to handle medical input uncertainty and imprecision. This algorithm predicts health severity accurately and individually. Proof of Authentication 2.0 (PoAh 2.0) consensus ensures data integrity and non-repudiation in the blockchain-enhanced architecture’s immutable, decentralised ledger. By adding XAI (LIME/SHAP) to give local and alternative answers, the system stops being a black box and becomes an open collaborator. Edge-cloud integration improves performance by lowering delay for important real-time alerts. Security tests show that the PoAh 2.0 system works well and can be expanded by quickly building and verifying blocks. This makes sure that strict privacy rules are followed while keeping the good predictive performance of a cardiac arrest prediction case study. This platform sets a new standard for interpretable, safe, and responsive AI-driven healthcare.</p>