The rapid expansion of international trade and digitalization has significantly increased the complexity of trade finance operations in India, exposing financial institutions to operational and financial risks such as fraud, documentation errors, credit defaults, and regulatory non-compliance. India’s merchandise exports reached approximately $437 billion in FY 2023–24, highlighting the need for a secure and efficient trade finance system. However, traditional systems remain largely manual and fragmented, resulting in inefficiencies and higher risk exposure. This study examines the role of Artificial Intelligence (AI) in mitigating these risks within India’s trade finance sector. The findings reveal that AI-driven technologies, including machine learning, predictive analytics, and natural language processing, reduce documentation errors from 12–15% to 3–5% and decrease processing time from 7–10 days to 1–2 days, improving efficiency by nearly 75%. Additionally, AI enhances fraud detection accuracy from 60–70% to 85–95% and reduces default rates by 20–30%, strengthening financial risk management. Transaction costs have also declined by approximately 35%, while compliance verification time has reduced by nearly 70%. The Indian trade finance market, valued at around $1.98 billion in 2024, reflects increasing AI adoption, while the global market is projected to reach $31.86 billion by 2030. Despite these benefits, challenges such as data privacy risks, cybersecurity threats, and regulatory gaps persist. The study concludes that AI is a critical enabler for enhancing efficiency, transparency, and resilience in India’s trade finance ecosystem, though its sustainable adoption requires strong governance frameworks and regulatory oversight.