Traditional structural health monitoring systems, such as visual inspection, ultrasonic testing, radiographic testing, and magnetic particle testing, are commonly used to detect damage and assess the integrity of structures. These methods effectively identify visible and certain internal flaws but often rely heavily on human expertise and specialized equipment, limiting their accuracy and accessibility. Additionally, they may not provide continuous real-time monitoring or effectively detect early-stage damage, necessitating advancements in SHM technologies for improved safety and performance. In this regard, the paper discusses the 3600 perspective of next-generation structural health monitoring, focusing on the applications of artificial intelligence (AI), IoT, and digital twins in civil infrastructure. It highlights the use of advanced machine learning (ML) techniques for efficient damage detection and predictive maintenance. The study examines an IoT-BIM framework for real-time SHM data visualization and processing, using cloud-based MySQL for immediate decision-making. It also reviews a human–machine collaboration system for rapid damage assessment and analyzes associated challenges.