Review of AI–IoT frameworks for modular integrated construction: Toward zero-waiting coordination

Modular Integrated Construction (MiC) increasingly relies on real-time coordination across design, production, transportation, and installation. However, current practices remain constrained by fragmented data, inconsistent semantics, and limited cross-stage feedback. This paper examines how AI-IoT technologies can jointly support zero-waiting coordination, aiming to minimize avoidable delays and maintain consistent workflow under dynamic conditions. A mixed-review approach integrating bibliometric, systematic review, and thematic-coding was conducted on 79 articles following Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The findings organize existing advances into four functional domains: cyber-physical sensing for continuous state capture, intelligent-analytics for prediction and interpretation, semantic-integration for cross-stage data alignment, and adaptive-decision coordination for feedback-driven regulation. The review also clarifies the mechanisms that link these domains and identifies gaps that hinder the integration of processes. By synthesizing fragmented developments into a process-oriented framework, this paper enhances current understanding and provides practical insights for developing reliable AI-IoT-enabled coordination strategies in MiC. • AI-IoT components jointly enable continuous sensing, prediction, and feedback across MiC stages. • Unified semantic integration enhances cross-stage data interoperability for real-time MiC coordination. • Intelligent analytics support takt stability through dynamic bottleneck prediction and adaptive scheduling. • Real-time sensing improves installation accuracy, risk detection, and module alignment reliability. • Integrated AI-IoT framework strengthens zero-waiting flow through synchronized lifecycle information exchange.