The integration of Artificial Intelligence (AI) into Anti-Money Laundering (AML) frameworks represents a transformative shift in combating financial crimes. Initially celebrated for its innovative potential, AI’s increasing presence in India’s financial sectors highlights a paradoxical reality. On the one hand, advanced predictive algorithms and data analytics have revolutionized the detection of illicit financial flows, bolstering traditional AML mechanisms. On the other hand, the rise of generative AI presents troubling possibilities for misuse, including the ability to obscure transactions and exploit regulatory gaps, thereby introducing significant legal complexities. This paper critically examines AI’s dichotomous role in AML, acting as both a safeguard against financial crime and a potential enabler of such activities. It explores the uncharted legal terrain of AI-generated evidence in money laundering cases, focusing on the challenges of authenticity, reliability, and the chain of custody of such evidence. The opaque, “black box” nature of AI algorithms compounds these issues, raising fundamental questions about transparency, accountability, and their implications for legal due process. Through doctrinal and qualitative analysis, this research draws comparative insights from global jurisdictions to evaluate AI governance and evidentiary standards within India’s evolving legal landscape. The findings highlight the urgent need for robust policy interventions to address the lacunae in the admissibility and governance of AI-generated evidence in money laundering cases. By safeguarding constitutional rights and upholding the integrity of legal proceedings, this paper proposes a roadmap for the responsible integration of AI into India’s AML framework.