This paper examines the transformative potential of integrated RFID-EPCIS-AI systems for quality assurance in automotive supply chains. The study demonstrates how combining RFID sensor networks with EPCIS standards and artificial intelligence enables real-time monitoring of critical quality parameters (humidity, shock, temperature) across the entire supply chain - from supplier warehouses to final assembly. Key findings reveal three core advantages of this technological integration: First, it establishes unprecedented supply chain transparency through automated data capture and standardized information exchange. Second, machine learning algorithms applied to RFID-EPCIS data streams enable predictive quality control, identifying potential defects before they impact production. Third, the system significantly improves operational efficiency by reducing quality incident response times and minimizing manual inspection requirements. The research highlights how AI-driven analysis of sensor data can detect subtle patterns correlating transportation conditions (e.g., vibration levels) with future failure risks. Case evidence shows these systems facilitate proactive interventions at critical control points, transforming traditional reactive quality management into a preventive approach. This study contributes to both academic literature and industry practice by providing a comprehensive framework for implementing smart quality assurance systems that combine physical sensor networks with digital traceability standards and advanced analytics. The results demonstrate measurable improvements in defect prevention, compliance assurance, and supply chain resilience in automotive manufacturing contexts.