Automotive manufacturers face growing challenges in managing supplier quality and compliance with the IATF 16949 standard across complex, multi-tier supply chains. This paper proposes an autonomous supplier quality ecosystem that leverages blockchain technology (both permissioned and public networks) and federated artificial intelligence (AI) to automate and enforce IATF 16949 compliance in the North American automotive industry. The approach integrates decentralized identity (DID) and verifiable credentials for secure supplier and part identification, smart contracts for automated quality control and auditing, and advanced AI techniques (including deep metric learning and neural networks) for real-time tool wear detection, supplier performance scoring, and anomaly prediction. The ecosystem’s architecture supports engagement across multiple supplier tiers, enabling secure data collaboration and real-time decision-making while preserving data privacy. We evaluate how this architecture improves traceability, predictive quality, and compliance reporting. Technical case studies – such as deep metric learning for stamping tool condition diagnostics and federated anomaly detection for predictive maintenance – demonstrate the potential for improved defect prevention and reduced downtime. Blockchain immutability ensures data integrity of quality records, while federated learning enables collective model improvement without exposing proprietary data. We present high-level system architectures, process flowcharts, and performance benchmarks showing that the proposed ecosystem can significantly reduce quality issue response time and compliance auditing effort. The solution is grounded in North American automotive quality requirements and IATF 16949 audit mechanisms, illustrating how next-generation Quality 4.0 tools can enhance continuous improvement and supplier oversight. By combining blockchain’s trustworthiness with AI’s predictive power, the autonomous ecosystem offers a novel pathway to strengthen compliance, transparency, and resilience in automotive supply chain quality management.