Application of Machine Learning in Predicting Project Cost Overruns: A Review

Abstract

Cost overrun is one of the main problems in project management that has a significant impact on the success of construction projects. High project complexity, uncertainty of field conditions, design changes, and external factors such as inflation and supply chain disruptions make predicting cost overruns a challenge that requires a more sophisticated analytical approach than conventional methods. The development of Machine Learning (ML) technology has opened new opportunities to improve the accuracy of project cost predictions through the use of historical data and complex pattern analysis. This study aims to review the development of research related to the application of Machine Learning in predicting project cost overruns, identify the most widely used algorithms, influential predictor variables, and evaluate the advantages and limitations of each approach. The research method is carried out through a systematic literature review of scientific publications indexed by Scopus in the period 2015–2025. The study focuses on various ML algorithms, including Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Gradient Boosting, and Extreme Gradient Boosting (XGBoost), which are used in project cost prediction. The study results show that Random Forest and XGBoost to produce higher levels of accuracy than traditional statistical methods or single ML models. This study concludes that Machine Learning has great potential in supporting early warning systems and data-driven decision-making to control the risk of project cost overruns.