The use of blockchain-related operations and the fast pace of the development of the digital financial system has made financial crimes larger and more intricate, and the conventional systems of detection based on the rules have become progressively less effective. This study suggests an AI-based financial crime analytics solution, which combines graph intelligence with blockchain trace forensics to improve the quality of fraud detection and the depth of investigating it. They are financial and blockchain transaction data, they are modeled as heterogeneous graphs allowing capturing relational, structural and temporal dependence between accounts and wallets. The architecture is a combination of relational learning, by using Graph Neural Networks, temporal behavior analysis, using Long Short-Term Memory (LSTM), interpretable risk scoring, using Random Forest, and page-rank-based anomaly detection, which was created to identify suspicious entities in the early supported by analyses and prediction. Large-scale financial and blockchain experimental evaluation shows that the proposed combined framework has an accuracy of 96.2, an F1-score of 0.94, and false-positive rate of 4.9, which beats standalone models and similar research by a margin of 4-10%. The results of blockchain-type of experimentation also indicate that illicit activity can be detected at 95.4% with transaction tracing over 9 hops, which is much better in terms of improving forensic visibility. The findings verify that the integration of AI-based graph analytics and blockchain forensics would be a scalable, resolute and future-appropriate answer to counter high-tech financial crimes in dynamic virtual worlds