Abstract: With the continuous increase in the complexity and technological sophistication of advanced equipment, conventional rule-based and predefined testing paradigms have become increasingly inadequate for meeting the high-precision and multidimensional testing requirements of modern equipment. The deep integration of artificial intelligence with testing technologies is driving a systemic transformation in the field of equipment testing. Focusing on the theoretical framework and technological evolution of intelligent equipment testing, this paper systematically reviews relevant domestic and international standards, policy documents, and representative studies. On this basis, the conceptual boundaries and scope of application of intelligent testing are examined and defined, and four core autonomous characteristics in intelligent equipment testing are identified. Covering the entire equipment-testing workflow from design, sensing, and signal acquisition to analysis, this paper further examines the intelligent technologies and recent advances in four key areas: intelligent testability design, intelligent sensing, intelligent signal acquisition, and intelligent data analysis. Finally, five future research directions are proposed: exploring advanced testing mechanisms, expanding full-domain testing scenarios, developing lightweight testing technologies, establishing a unified testing framework, and enhancing generalizable cognitive capabilities across different types of equipment. These directions outline a critical pathway for the evolution of intelligent testing from technological exploration toward a coherent academic discipline and provide guidance for its future development and further research.