The construction industry has traditionally been characterized by fragmented workflows, productivity challenges, cost overruns, safety risks, and environmental concerns. Despite significant technological advancements in other sectors, construction has historically lagged in digital transformation due to complex project environments, heterogeneous data sources, and resistance to technological adoption. In recent years, Machine Learning (ML), a subset of Artificial Intelligence (AI), has emerged as a transformative technology capable of addressing these challenges by enabling data-driven decision-making, predictive analytics, automation, and intelligent resource management. This paper investigates the role of machine learning in modern construction management and examines its contributions toward enhancing productivity, quality, and sustainability. Through a comprehensive review of existing literature, the study synthesizes research findings related to project scheduling, cost estimation, risk assessment, quality control, safety management, resource optimization, and sustainable construction practices. The review highlights how advanced ML techniques, including supervised learning, unsupervised learning, deep learning, reinforcement learning, and hybrid AI models, are increasingly integrated with Building Information Modeling (BIM), Internet of Things (IoT), digital twins, drones, and cloud-based construction platforms. Furthermore, the study critically evaluates the strengths and limitations of existing research and identifies emerging trends shaping Construction 4.0 and Construction 5.0 paradigms. Findings indicate that ML-driven construction management significantly improves project performance by reducing delays, minimizing cost overruns, improving quality assurance, enhancing worker safety, and optimizing energy and resource utilization. However, challenges related to data quality, model interpretability, cybersecurity, interoperability, and workforce readiness continue to hinder widespread implementation. The paper contributes to both theory and practice by proposing a conceptual understanding of machine learning-enabled construction ecosystems and identifying future research opportunities that support sustainable and intelligent infrastructure development.

Keywords: Machine Learning, Construction Management, Construction 4.0, Building Information Modeling, Sustainability, Predictive Analytics, Artificial Intelligence