This review article explores the transformative potential of advanced risk management models in enhancing the resilience and efficiency of supply chain finance (SCF). It examines the application of Artificial Intelligence (AI), Machine Learning (ML), Big Data analytics, and blockchain technology in transitioning from traditional reactive strategies to proactive and predictive risk management approaches. Despite their advantages, the paper addresses challenges, model limitations, and regulatory and ethical considerations. It also discusses recommendations for effective deployment and areas for future research. Supply chain finance has evolved from basic trade finance instruments to sophisticated financing solutions that optimize working capital and enhance liquidity. Traditional SCF strategies focused on mitigating risks like payment delays and supplier bankruptcy, but global supply chains are now more susceptible to various risks, including geopolitical tensions and pandemics. Risk management in SCF has shifted from reactive measures to proactive and predictive modeling, facilitated by advancements in data analytics, AI, and blockchain technology. The paper discusses theoretical foundations such as Modern Portfolio Theory, Principal-Agent Theory, and credit risk models, which have been adapted for SCF. Recent advances include AI and ML for predicting disruptions, blockchain for enhancing transparency, and Big Data analytics for real-time monitoring. However, gaps remain in integrating these models, addressing their practical impact, and considering ethical and regulatory implications. Advanced risk management models leverage AI, ML, Big Data, and blockchain to identify, assess, and mitigate risks more effectively. These models offer improvements over traditional approaches in efficiency, accuracy, and reliability. However, challenges include technological infrastructure, data privacy, model obsolescence, and ethical considerations. The paper concludes that while advanced risk management models offer significant benefits, their implementation requires addressing technological, regulatory, and ethical challenges. Future research should focus on developing robust models, addressing ethical implications, and exploring emerging technologies like quantum computing and IoT. The paper emphasizes the need for collaboration, continuous innovation, and research to realize the full potential of advanced risk management models in SCF.This review article explores the transformative potential of advanced risk management models in enhancing the resilience and efficiency of supply chain finance (SCF). It examines the application of Artificial Intelligence (AI), Machine Learning (ML), Big Data analytics, and blockchain technology in transitioning from traditional reactive strategies to proactive and predictive risk management approaches. Despite their advantages, the paper addresses challenges, model limitations, and regulatory and ethical considerations. It also discusses recommendations for effective deployment and areas for future research. Supply chain finance has evolved from basic trade finance instruments to sophisticated financing solutions that optimize working capital and enhance liquidity. Traditional SCF strategies focused on mitigating risks like payment delays and supplier bankruptcy, but global supply chains are now more susceptible to various risks, including geopolitical tensions and pandemics. Risk management in SCF has shifted from reactive measures to proactive and predictive modeling, facilitated by advancements in data analytics, AI, and blockchain technology. The paper discusses theoretical foundations such as Modern Portfolio Theory, Principal-Agent Theory, and credit risk models, which have been adapted for SCF. Recent advances include AI and ML for predicting disruptions, blockchain for enhancing transparency, and Big Data analytics for real-time monitoring. However, gaps remain in integrating these models, addressing their practical impact, and considering ethical and regulatory implications. Advanced risk management models leverage AI, ML, Big Data, and blockchain to identify, assess, and mitigate risks more effectively. These models offer improvements over traditional approaches in efficiency, accuracy, and reliability. However, challenges include technological infrastructure, data privacy, model obsolescence, and ethical considerations. The paper concludes that while advanced risk management models offer significant benefits, their implementation requires addressing technological, regulatory, and ethical challenges. Future research should focus on developing robust models, addressing ethical implications, and exploring emerging technologies like quantum computing and IoT. The paper emphasizes the need for collaboration, continuous innovation, and research to realize the full potential of advanced risk management models in SCF.