The advent of the big data era has introduced massive data applications characterized by four defining attributes—Volume, Variety, Velocity, and Value (4V)—posing 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, thereby 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, diagnostics, and workload management), and (3) machine learning-based high-performance 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 work systematically investigates intelligent databases through an innovative standardization-centric framework, delineating common processing paradigms across core research themes—interaction paradigms, management architectures, and kernel design. By examining standardized processes, interfaces, and collaboration mechanisms, it uncovers the core logic enabling database self-optimization, synthesizes current research advancements, and critically assesses persistent technical challenges and prospects for future development.