Projects are becoming increasingly complex and exposed to multiple sources of uncertainty, making effective risk management essential. At the same time, AI and data-driven technologies are changing the landscape of decision-making across domains. These approaches offer opportunities and challenges for the wider adoption and implementation of predictive and adaptive Project Risk Management (PRM). However, despite the rapid growth of AI applications, their integration into PRM frameworks remains limited and conceptually fragmented. This thesis conducts a Systematic Literature Review (SLR) to provide an overview of existing AI and data-driven applications for PRM and to identify gaps and opportunities for adoption. Fourteen systematic literature reviews between 2003 and 2025 covering domains such as construction, infrastructure, innovation management and small-medium enterprises were collected from Scopus, in accordance with the PRISMA 2020 guidelines.