This article, a systematic literature review on ‘Predictive Risk Management in the Supply Chain’, presents significant findings. It analyses the current state of research and identifies trends, challenges, and solutions. The analysis is based on 13 scientific articles from 2021 to 2024. The results underscore the increasing use of technologies such as artificial intelligence (AI), machine learning (ML), and big data analytics (BDA) for risk prediction and management in supply chains. These innovative approaches enable early risk identification, process optimisation, and informed decision-making, offering a promising supply chain risk management future. The main challenges identified are the complexity of supply chains, the subjectivity of human risk assessments, and the implementation of new technologies and data analytics. The studies propose various solutions, such as integrating AI and ML into risk management, using BDA for improved transparency and predictions, and developing technology-enabled risk assessment methodologies. The research highlights the potential of modern technologies for more efficient, agile, and resilient supply chain risk management (SCRM), instilling a sense of optimism in the audience.