(untitled) (International Journal of Latest Technology in Engineering Management & Applied Science, 2025)
The construction industry continues to face persistent challenges such as cost and time overruns, safety risks, low productivity, and labor shortages. Despite its economic significance, the sector remains one of the least digitized globally, limiting its ability to address these challenges effectively. Artificial Intelligence (AI), as an advanced digital technology, has demonstrated the potential to transform traditional construction practices, similar to its impact on manufacturing, retail, and telecommunications. This study presents a systematic literature review of AI applications in the construction industry, aiming to identify dominant application areas, commonly adopted AI techniques, and existing research gaps. The review was conducted in accordance with the PRISMA 2020 guidelines to ensure transparency and methodological rigor. Relevant peer-reviewed studies published between 2015 and 2025 were identified through structured searches of Scopus, Web of Science, Science Direct, and Google Scholar. Over 200 records were initially retrieved. After duplicate removal and multi-stage screening, 15 studies met the inclusion criteria and were selected for in-depth qualitative analysis. The findings indicate that AI has been applied across key construction domains, including structural health monitoring, safety and risk management, design and pre-construction planning, sustainability, waste management, and on-site robotics. Machine learning and neural network-based approaches were the most frequently used techniques. While the reviewed studies demonstrate AI’s strong potential to improve efficiency, safety, and sustainability in construction projects, significant challenges remain, particularly regarding data quality, lack of standardization, system integration, and user trust. This review provides a consolidated overview of AI applications in construction and outlines critical directions for future research and industry adoption.