Architectural, engineering and construction (AEC) industry has large amounts of data, the analysis of which can predict risks such as project delays, project budget overruns, low resource use efficiency, environmental damage, reduced safety, and the like. Research has shown that 98% of construction projects are realized with the contracted budget and deadline exceeded. This paper aims to show the significant potential and possibilities of applying artificial intelligence (AI) in the AEC industry, pointing out the benefits and obstacles faced by the AEC industry during the implementation of AI. The benefits of applying AI in the AEC industry relate to increasing efficiency, reducing costs, increasing productivity, increasing safety, better planning and providing timely and accurate information. The key obstacles that hinder the implementation of AI in practice are high implementation costs, incomplete data, job loss due to automation, mistrust of AI technology, cyber vulnerability, and lack of understanding field of AI.

The construction sector is a major consumer of raw materials and a significant source of greenhouse gas emissions, necessitating data-driven approaches to support circular economy implementation and sustainable project management. This study develops an integrated framework combining machine learning-based material stock prediction, carbon footprint assessment, and Environmental, Social, and Governance (ESG) performance evaluation for construction projects. A dataset of 128 residential buildings was compiled from official use-permit documentation. After dimensionality reduction using variance filtering and Spearman correlation analysis, 25 regression algorithms were evaluated to estimate quantities of concrete, reinforcement, and brick products. The K-Nearest Neighbor (KNN) Regressor achieved the best predictive performance, with mean absolute percentage errors of 10.64% for concrete, 10.23% for reinforcement, and 16.05% for brick products. Predicted material quantities were used to calculate CO₂ emissions across materialization, demolition, and disposal phases under linear and circular scenarios. The results indicate that circular economy implementation significantly reduces total emissions, particularly for concrete, with reductions of up to 97% under idealized full-substitution conditions, representing an upper-bound estimate. ESG assessment using the Delphi method identified environmental indicators as the most significant sustainability dimension. The proposed framework enables early-stage emission estimation and supports informed decision-making toward low-carbon and resource-efficient construction practices.