In the era of big data and complex information systems, traditional database query methods often struggle to provide accurate and relevant results, particularly in large-scale or unstructured datasets. This paper explores the integration of artificial intelligence (AI) techniques to enhance data retrieval in modern database systems, focusing on improving query accuracy and relevance. AI-driven approaches, such as natural language processing (NLP), machine learning (ML), and deep learning, enable databases to better understand query intent, deliver semantically meaningful results, and adapt to user preferences. This paper also examines how AI can optimize query performance through contextual awareness, semantic search, and personalized retrieval, which go beyond conventional indexing and query optimization strategies. Additionally, it discusses real-world applications of AI-enhanced data retrieval, including search engines, recommendation systems, and intelligent database systems. While AI offers significant advantages, challenges such as data quality, model interpretability, and computational scalability remain. The paper concludes with an outlook on the future of AI in database systems, highlighting emerging trends like hybrid query systems and the potential role of quantum computing in revolutionizing data retrieval processes. By bridging AI with database technologies, this research provides valuable insights into the transformative potential of intelligent data retrieval for next-generation information systems.