Supply chain risk assessment (SCRA) is pivotal for ensuring resilience in increasingly complex global supply networks. While existing reviews have explored traditional methodologies, they often neglect emerging artificial intelligence (AI) and machine learning (ML) applications and mostly lack combined systematic and bibliometric analyses. This study addresses these gaps by integrating a systematic literature review with bibliometric analysis, examining 1,903 articles (2015-2025) from Google Scholar and Web of Science, with 54 studies selected through PRISMA guidelines. Our findings reveal that ML models, including Random Forest, XGBoost, and hybrid approaches, significantly enhance risk prediction accuracy and adaptability in post-pandemic contexts. The bibliometric analysis identifies key trends, influential authors, and institutional contributions, highlighting China and the United States as leading research hubs. Practical insights emphasize the integration of explainable AI (XAI) for transparent decision-making, real-time data utilization, and blockchain for traceability. The study underscores the necessity of dynamic strategies, interdisciplinary collaboration, and continuous model evaluation to address challenges such as data quality and interpretability. By synthesizing AI-driven methodologies with resilience frameworks, this review provides actionable guidance for optimizing supply chain risk management, fostering adaptability, and informing future research in evolving risk landscapes.

翻译：供应链风险评估（SCRA）对于确保日益复杂的全球供应网络韧性至关重要。现有综述虽已探讨传统方法，但常忽略新兴人工智能（AI）与机器学习（ML）应用，且大多缺乏系统性综述与文献计量分析的结合。本研究通过整合系统性文献综述与文献计量分析来填补这些空白，审查了来自Google Scholar和Web of Science的1,903篇文献（2015-2025年），并依据PRISMA指南筛选出54项研究。我们的研究结果表明，包括随机森林、XGBoost及混合方法在内的ML模型，显著提升了后疫情背景下风险预测的准确性与适应性。文献计量分析识别了关键趋势、有影响力的作者及机构贡献，凸显中国和美国作为领先的研究中心。实践洞见强调集成可解释人工智能（XAI）以实现透明决策、实时数据利用以及区块链技术提升可追溯性。本研究强调了动态策略、跨学科合作以及持续模型评估的必要性，以应对数据质量和可解释性等挑战。通过将AI驱动的方法与韧性框架相结合，本综述为优化供应链风险管理、增强适应性以及为不断演变的风险格局中的未来研究提供指导，给出了可操作的指引。