Decentralized Finance (DeFi) has emerged as one of the most transformative applications of blockchain technology, allowing users to access financial services such as lending, trading, and asset management without intermediaries. However, this innovation has also introduced new forms of security vulnerabilities, from flash loan attacks to oracle manipulations and smart contract bugs. In recent years, Artificial Intelligence (AI) models have shown potential to enhance blockchain ecosystems by providing predictive analytics, anomaly detection, and automated threat intelligence. This paper presents a detailed study on integrating AI and blockchain technologies to strengthen DeFi security. The proposed framework explores how machine learning algorithms, natural language processing, and immutable blockchain audit trails can be jointly used to detect, prevent, and record malicious activities. Real-world frameworks such as Chainalysis, Forta, SmartBugs, and OpenZeppelin are reviewed as supporting infrastructure. Through case studies and conceptual experimentation, the study highlights that coupling blockchain transparency with AI adaptability leads to significant improvements in response speed, detection accuracy, and trustworthiness. Finally, the paper discusses ethical, operational, and scalability considerations, outlining directions for future research in AI-driven decentralized security systems.