Blockchain technology has improved the digital transformation of the banking sector, boosting transaction security. Increasingly elaborate procedures are required to defend against increasingly sophisticated cyberattacks. The purpose of this research is to provide a machine learning-based anomaly detection system for blockchain-powered financial transactions. This system combines the analytical capability of machine learning with the reliability of blockchain to identify and prevent fraud. This assures the dependability and security of digital financial services. Using unsupervised machine learning, the proposed approach explores blockchain transaction patterns. These algorithms automate transaction data analysis and help identify fraud. The system's blockchain design allows it to evaluate hazards in real time and react quickly. This strategy ensures that defences stay effective as they develop by adapting security systems to new threats. Continue with care if this strategy works in a monitored online banking environment. Anomaly detection systems detect unusual financial activities, lowering the frequency of unreported fraudulent operations. The system's false-positive rate was low to nonexistent, suggesting that it seldom interfered with legitimate financial activities. To conclude, blockchain systems that use machine learning algorithms to detect anomalies impede digital currency scammers. The results demonstrate a considerable boost in financial transaction security, bolstering blockchain technology's use in banking and finance. This novel strategy is the first step towards changing banking security standards, and it has the potential to influence industry improvements. Furthermore, this approach may have an influence on future industrial breakthroughs.