The increasing reliance on artificial intelligence (AI) in high-stakes socio-technical systems ranging from healthcare and finance to energy and critical infrastructure has intensified demands for models that are not only accurate but also interpretable, verifiable, and trustworthy. Traditional deep learning, while powerful in predictive performance, often functions as a “black box,” limiting transparency and accountability. Conversely, symbolic reasoning frameworks offer interpretability but struggle with scalability and adaptability to complex, dynamic environments. Recent research has highlighted the promise of neuro-symbolic deep learning, which integrates the pattern-recognition capabilities of neural networks with the logical rigor of symbolic reasoning, thereby balancing performance with explainability. At the same time, ensuring verifiable and decentralized decision-making has become a pressing requirement in distributed socio-technical ecosystems where multiple stakeholders must collaborate without relying on centralized authorities. Blockchain consensus mechanisms, with their tamper-resistant ledgers and decentralized trust protocols, provide an architectural foundation for secure verification and transparent governance. By fusing neuro-symbolic AI with blockchain consensus, it becomes possible to design systems where decisions are interpretable through symbolic reasoning, auditable via blockchain records, and adaptable to evolving contexts through deep learning. This convergence addresses critical gaps in trust, accountability, and resilience for socio-technical systems operating under conditions of uncertainty and risk. The proposed paradigm establishes pathways for integrating explainable AI with decentralized infrastructures, enabling interpretable yet robust decision-making frameworks suitable for applications in domains such as autonomous healthcare, financial regulation, and critical infrastructure management.