The rapid expansion of digital financial ecosystems has intensified the need for secure, transparent, and intelligent fraud-mitigation mechanisms. This study examines the integration of AI-driven fraud detection models with blockchain-based transaction networks to provide a more resilient framework for combating modern financial threats. By leveraging machine learning–enabled anomaly detection, predictive analytics, and decentralized ledger infrastructures, the approach enhances real-time monitoring, improves data integrity, and reduces false-positive rates across high-velocity transaction environments. The fusion of AI intelligence with blockchain immutability delivers a scalable architecture capable of addressing evolving cyber risks while supporting secure automation through smart contracts. Findings highlight the potential of this hybrid model to strengthen trust, increase operational transparency, and accelerate the adoption of next-generation financial security systems.