AI-based software supply chain risk prediction focuses on identifying and assessing potential security risks associated with third-party software dependencies and components used in applications. It involves analyzing software package metadata, Software Bill of Materials (SBOM) records, known vulnerability information, and component characteristics to evaluate dependency security and estimate potential exposure to software supply chain threats. By combining gradient-boosted machine learning models with classification algorithms, this approach helps predict dependency vulnerability risks, categorize components according to their security risk levels, and prioritize components that require further investigation or remediation. The resulting assessments support informed dependency selection, proactive vulnerability management, improved software component visibility, and stronger security throughout the software development and maintenance lifecycle.