Artificial Intelligence in Preconstruction Cost Estimation: A Systematic Review

Reliable preconstruction cost estimation (PCE) is a fundamental stage to project planning and investment decision-making. However, the early-stage uncertainty and limited information make early-stage cost estimation challenging. Artificial intelligence has attracted growing interest as a way out of this impasse, producing a substantial empirical literature worth systematic examination. This study synthesizes the findings of 30 empirical studies published up to December 2025 using PRISMA protocols and examines the AI techniques, project types, dataset characteristics, validation practices, and model interpretability. The results show that artificial neural networks (ANNs) and hybrid approaches dominate the literature, with applications concentrated in building and transportation projects. Reported model performance is generally strong across commonly used evaluation metrics. However, several structural limitations persist: poor generalizability across project contexts, inconsistent validation procedures, limited adoption of explainable AI, and minimal integration of domain expertise. These factors, together, limit the transferability of existing models to real-world practice. This review contributes a structured methodological synthesis, maps the gaps that most limit progress, and proposes a conceptual AI–Expert Integration Framework to support estimation approaches that are more robust, interpretable, and decision-oriented. The findings offer both a current assessment of the field and a practical roadmap for advancing AI-driven PCE research.