This chapter examines the transformative role of artificial intelligence (AI) in improving risk management (RM) practices within construction projects. Traditional RM approaches, often based on subjective judgement and poor data structuring, face challenges in knowledge transfer and process automation. This chapter explores how AI, particularly machine learning (ML), can address these limitations and provide real-time, data-driven insights to identify, analyse and mitigate risk. The chapter categorises AI applications in RM into probabilistic and deterministic models. Probabilistic models, such as Bayesian networks, are highlighted for their ability to integrate subjective expert opinion with objective data, making them suitable for environments with limited or incomplete data. Deterministic models, including artificial neural networks and decision trees, are highlighted for their high accuracy and scalability in data-rich contexts. The effectiveness of these approaches is demonstrated through case studies of varying data availability, illustrating the strengths of each methodology in risk prediction and decision making. By combining theoretical insights with practical applications, the chapter underlines the potential of AI to revolutionise construction RM. It highlights the importance of selecting appropriate AI models based on data availability and problem complexity to enable safer, more efficient and cost-effective construction project management.