This paper examines how artificial intelligence (AI) and blockchain analytics can be operationalised as enforcement technologies to detect crypto tax evasion in India, while remaining compliant with evolving legal constraints on privacy and digital evidence. It situates the analysis within India’s post-2022 “virtual digital asset” (VDA) taxation architecture, including the statutory definition of VDA, the special charging and ring-fencing framework that taxes transfers at a flat rate with limited deductions, and the transaction-level reporting trail created through the one per cent tax deduction at source (TDS) mechanism on VDA transfers. It further maps the parallel expansion of anti-money laundering coverage to VDA service providers and explains how these compliance streams generate high-volume, high-granularity datasets suitable for automated risk scoring. On the technology side, the study details how blockchain forensics converts raw ledger data into investigable transaction graphs through address clustering, attribution, taint tracing, and typology-based risk signals, and how AI systems use these features to detect anomalies such as non-reporting, under-reporting, misclassification, offshore routing, chain-hopping, privacy-enhancing obfuscation, and circular or undervalued intra-group transfers. It argues that integrated models combining on-chain traces with off-chain records (exchange KYC, TDS data, FIU reports, and other regulatory filings) can reconstruct undeclared trading histories and prioritise cases with higher revenue risk more effectively than manual scrutiny.