The advent of the big data era has introduced massive data applications characterized by four defining attributes: volume, variety, velocity, and value. These attributes pose revolutionary challenges to conventional data acquisition methods, management strategies, and database processing capabilities. Recent breakthroughs in artificial intelligence (AI), particularly in machine learning and deep learning, have demonstrated remarkable advancements in representation learning, computational efficiency, and model interpretability, thus offering innovative solutions to these challenges. This convergence of AI and database systems has given rise to a new generation of intelligent database management systems, which integrate AI technologies across three core architectural layers: (1) natural language interfaces for user interaction, (2) automated database administration frameworks (including parameter tuning, index recommendation, database diagnostics, and workload management), and (3) machine learning-based efficient and scalable components (such as learned indexes, adaptive partitioning, query optimization, and scheduling). Furthermore, new intelligent component application programming interfaces (APIs) have lowered the integration barrier between AI and database systems. This study systematically investigates intelligent databases through a standardization-centric framework, delineating common processing paradigms across the research themes of interaction paradigms, management architectures, and kernel design. By examining standardized processes, interfaces, and collaboration mechanisms, this study uncovers the core logic enabling database self-optimization, reviews current research advancements, and provides an in-depth analysis of the technical challenges and prospects for future development.