Applications of machine learning and artificial intelligence in construction project cost prediction: a scientometric analysis and qualitative review

Accurate construction project cost prediction directly affects investment decision-making, resource allocation efficiency, and project risk management. Under increasingly complex project environments, traditional estimation methods face limitations in handling high-dimensional data, nonlinear relationships, and dynamic fluctuations. Accordingly, this study conducts a scientometric analysis and qualitative review of the application of machine learning and artificial intelligence in construction cost prediction. The PRISMA framework was adopted for systematic literature retrieval and screening to ensure the transparency and reproducibility of the dataset construction process. Web of Science and Scopus were used as the primary data sources, from which 138 relevant articles were ultimately selected. Subsequently, scientometric analysis, knowledge graph visualization, and qualitative analysis were integrated to systematically examine the research evolution, knowledge structure, model application characteristics, and future development trends in this field. Findings indicate that since 2021, the field has entered a rapid expansion phase, with research focus shifting from early single-model validation toward integrated, deep, and hybrid approaches. ML and AI methods more effectively capture complex relationships influencing construction costs and demonstrate superior predictive accuracy and scenario adaptability compared with traditional methods. However, practical application remains constrained by unstable data quality, limited cross-regional generalization, and low model interpretability. By integrating scientometric results with content analysis, this study not only elucidates the evolution of research hotspots but also distills the mechanisms by which project attributes, design decisions, resource price fluctuations, and external environmental disturbances collectively drive construction costs. The findings provide a comprehensive review framework and offer guidance for optimizing predictive models, advancing multi-source data integration, and supporting intelligent decision-making in construction management.