The rapid growth of artificial intelligence and machine learning applications in project management is transforming the prediction of key performance indicators (KPIs). This has led to increased interest from both practitioners and the academic community, as reflected in the growing number of publications, creating a need for a comprehensive review. This paper presents a systematic scoping review of 688 peer-reviewed publications spanning three decades, analyzing the application of machine learning methods for predicting project KPIs across industries. The study aims to provide a comprehensive overview of current knowledge, offering insights and future research directions to advance the field and encourage greater innovation and efficiency in project management. The review focuses on identifying key patterns and trends in the data and exploring the potential of machine learning to enhance project outcomes. It also addresses the challenges and limitations of adopting these techniques. It reveals that machine learning applications are predominantly focused on cost and schedule performance prediction, with neural network-based approaches emerging as the most widely used techniques across industries. The findings indicate consistent improvements in predictive performance in complex project environments, while highlighting persistent challenges in data quality, model interpretability, and integration with existing project management systems.
Polaszek et al. (Sat,) studied this question.