ABSTRACT Construction and demolition waste (C&DW) represents up to 40% of global solid waste, posing a significant barrier to achieving circular economy (CE) objectives and the Sustainable Development Goals (SDGs), particularly SDG 11 and SDG 12. However, construction waste management (CWM) systems remain constrained by fragmented data environments, inconsistent key performance indicators (KPIs), and limited analytical decision‐support tools. This study systematically reviews 177 peer‐reviewed publications (2013–2025) to evaluate the role of artificial intelligence (AI) in enabling circular and sustainable CWM. The review shows that AI‐based forecasting, classification, and optimisation techniques can improve waste diversion rates by 10%–25%, reduce embodied carbon by up to 30%, and strengthen KPI monitoring. Moreover, integrating Building Information Modelling (BIM), Internet of Things (IoT) sensor networks, and Digital Twin technologies enhances data interoperability and operational scalability. Based on these insights, the study proposes a five‐tier AI‐CWM framework and a research‐to‐practice roadmap supporting scalable CE implementation and SDG‐aligned performance in the construction sector.
Elnabwy et al. (Fri,) studied this question.