The rapid digital transformation of healthcare systems has led to an unprecedented increase in the generation and exchange of sensitive patient data across distributed clinical environments. However, traditional centralized healthcare information systems face critical challenges related to data privacy, interoperability, security vulnerabilities, and inefficient clinical decision-making processes. To address these limitations, this paper proposes an intelligent blockchain-driven machine learning architecture designed to enable privacy-preserving clinical decision support in healthcare networks. The study integrates distributed ledger technology with advanced machine learning mechanisms to ensure secure data sharing, decentralized access control, and real-time predictive analytics for clinical decision-making.

The proposed architecture leverages blockchain’s immutable ledger structure to guarantee data integrity and traceability while employing machine learning models for intelligent pattern recognition and predictive diagnostics. Existing frameworks such as MedRec highlight the feasibility of blockchain in managing medical permissions and electronic health records securely in decentralized environments (Azaria et al., 2016). Building upon such foundational models, this research introduces a hybrid framework that enhances scalability, privacy preservation, and computational efficiency through distributed AI processing.

The study further synthesizes existing literature to identify gaps in interoperability, latency reduction, and secure AI integration within blockchain-based healthcare ecosystems. A conceptual architecture is developed to address these challenges through modular components including data encryption layers, smart contract-based access control, and federated learning-enabled predictive engines. The findings indicate that the integration of blockchain and machine learning significantly improves trust, transparency, and diagnostic accuracy in healthcare systems.