The power of blockchain technology to provide decentralized networks accountability and transparency has transformed several industries. However, there are limitations to traditional blockchain systems, like data tampering and insufficient real-time validation capabilities. The study proposes a ground-breaking approach to solving these issues by integrating AI algorithms to enhance accountability and transparency in blockchain ecosystems. The proposed system improves consensus processes, identifies abnormalities, and validates transactions in real-time by using AI-driven methods. Using rigorous data gathering, pre-processing, model selection, and continuous review, the system outperforms existing systems in terms of fraud detection accuracy, false positives and negatives, and transaction validation performance. Through a significant increase in security, scalability, and efficiency, the results indicate the potential of AI-driven blockchain solutions to support trust and reliability in transaction processing and fraud detection. The proposed system outperforms existing systems with a 95% fraud detection rate, a 5% false positive rate, and a 5% false negative rate. Furthermore, the scalability and efficacy of the proposed AI-enhanced blockchain system are demonstrated by the significant improvements in transaction validation performance across a variety of transaction volumes.