To survive in the competitive world and to succeed in today’s global marketplace, Companies need to run their enterprise like fine tuned engines to sustain growth. Artificial Intelligence technologies are well positioned to conquer these challenges and to better manage their supply chain while improving cost and enhancing efficiency, operation, performance and customer experience. Still AI is facing many challenges in supply chain management supplier selection, data quality and integrating data hence it makes decision making very complex and time consuming. To overcome these challenges a digital representation using Knowledge graph modeling in supply chain management allows one to get from data insight quickly. In the rapidly evolving landscape of supply chain management, the selection of reliable suppliers remains paramount. This research introduces an innovative approach to supplier recommendation by synergistically combining Graph Data Science (GDS) and traditional Artificial Intelligence (AI) methodologies, all within the secure environment of a private block chain network. Our hybrid intelligence model leverages GDS to unravel intricate supplier relationships, while AI algorithms provide predictive insights, ensuring precise and actionable supplier recommendations. The integration with a private block chain network enhances transparency, security, and traceability, offering a tamper-proof and verifiable recommendation system. Preliminary results indicate that this hybrid model surpasses standalone GDS or AI systems in accuracy, robustness, and scalability. This study not only underscores the transformative potential of integrating GDS, AI, and block chain but also sets a precedent for future endeavors in data-driven supply chain optimization.