Supply chain health encompasses an organization's enduring capacity to deliver value across cost efficiency, service reliability, agility, and resilience. This paper proposes a five-stage Supply Chain Health KPI Maturity Framework mapping performance measurement evolution from reactive, siloed indicators to predictive, cross-functional strategic health signals. A composite Supply Chain Health Index (SCHI) enables quantitative self-assessment across six weighted dimensions: KPI Strategic Alignment, Cross-Functional Integration, Data Timeliness, Financial Linkage, Decision Governance, and Technology Enablement. Drawing on established maturity model literature, the SCOR framework, and the Balanced Scorecard tradition, the study argues that KPI evolution is simultaneously a diagnostic marker of supply chain maturity and a structural mechanism that advances it. The role of artificial intelligence and machine learning (AI/ML) as a maturity accelerator is examined, with evidence that predictive analytics, ML-based anomaly detection, and generative AI capabilities are reshaping performance measurement from reactive reporting to autonomous intelligence. Four industry use cases and a practical implications roadmap are provided.