The rise of artificial intelligence-driven synthetic media, particularly deepfakes, has introduced a new frontier in financial crime: neural laundering. This phenomenon involves the use of AI-generated content—such as forged identities, manipulated voiceprints, and fabricated biometric data—to facilitate money laundering within decentralized and virtual ecosystems. As digital financial platforms such as cryptocurrency exchanges, metaverse environments, and decentralized finance (DeFi) networks proliferate, so do the methods used to obscure illicit financial flows. Traditional anti-money laundering (AML) mechanisms, largely reliant on structured data and rule-based systems, struggle to detect and mitigate the threats posed by these evolving laundering techniques. Neural laundering operates by weaponizing anonymity, algorithmic opacity, and borderless virtual assets, enabling launderers to bypass know-your-customer (KYC) protocols and embed fraudulent transactions within legitimate ecosystems. This paper explores the convergence of deepfake technology with financial laundering tactics, identifying key technological enablers such as synthetic identity generation, smart contracts, and crypto mixers. It further evaluates the effectiveness of contemporary AI-driven detection systems—including convolutional neural networks (CNNs), graph neural networks (GNNs), and explainable artificial intelligence (XAI) tools—in countering such threats. The paper also critically examines the legal and ethical implications of deploying advanced surveillance and detection systems within privacy-centric virtual domains. Drawing from recent literature and case studies, this study proposes an integrated framework that combines real-time blockchain analytics, AI explainability, and updated regulatory practices to detect and deter neural laundering. The urgency of developing agile, intelligence-driven compliance strategies is emphasized as financial ecosystems become increasingly digital, complex, and susceptible to misuse through AI-powered obfuscation techniques.