Abstract

This research project focuses on how machine learning and artificial intelligence help with different stages of construction projects. It is feasible to reduce hazards, estimate expenses more precisely, and even build sustainable structures through machine learning. The study also highlights how artificial intelligence excels at analyzing massive amounts of data to better plan and carry out projects in general, foresee problems, and distribute resources in the most efficient manner. The use of AI and machine learning technology is causing a significant transformation in the building industry. The building industry is changing due to the integration of machine learning and artificial intelligence. By monitoring construction sites and identifying threats early, AI-powered solutions also increase safety. Artificial intelligence and machine learning algorithms enable prompt decision-making, improve safety procedures, maximize resource usage, and offer predictive analytics. These solutions increase efficiency and lower risks by using big data to identify future project delays, cost overruns, and equipment breakdowns. The results show that those involved in building projects are looking for artificial intelligence tools to help with quantitative processes, especially those related to risk management, quality control, scope, schedule, and cost. AI-driven automation is also encouraging creativity, increasing productivity, and simplifying monotonous chores. This article focuses on how artificial intelligence and machine learning have significantly contributed to construction projects to produce more intelligent, sustainable building processes. It emphasizes challenges and outlines potential future integration options for these technologies. Finally, the article underlines the revolutionary power of artificial intelligence and machine learning in building. It is paving the way for a future in which the built environment is more efficient, safe, and sustainable. The research presents an academic contribution by conducting a complete literature review, categorizing artificial intelligence and machine learning applications based on the life cycle of a building project, and identifying suitable deployment sites at various stages