AI-driven financial crime analytics is redefining regulatory compliance by shifting detection from reactive rule-based monitoring to proactive probabilistic intelligence. Financial crime—money laundering, sanctions evasion, synthetic identity fraud, ransomware financing, and cross-chain asset obscuring has become faster, more automated, and technically sophisticated than current compliance infrastructure can handle. This study proposes a novel compliance-first analytics architecture that fuses predictive modeling, blockchain transaction forensics, graph intelligence, smart-contract tracing, risk-propagation modeling, and real-time anomaly profiling to strengthen compliance accuracy, auditability, and enforcement readiness. Using supervised learning, “graph neural networks (GNN)”, transformer-based behavioral profiling, and multi-chain forensic tagging, the framework detects illicit capital movement earlier, maps attribution failures across decentralized ledgers, and improves compliance decision quality while preserving explainability for regulators. The results indicate that AI embeddings improve fraud-pattern discovery by 220–300%, reduce false compliance flags by 45–55%, and enable 3–6-week earlier risk detection versus traditional compliance engines. The study contributes a scalable, regulator-friendly, automated compliance system capable of tracing illicit flows even when adversaries use mixers, privacy chains, or cross-chain bridges..