Projects play a crucial role in the success and development of industries, organizations and businesses, hence making project management an important practice which needs to be up to date with new trends and modern technology such as artificial intelligence (AI). With the advent of artificial intelligence there have been a number of studies aimed to design and introduce new ways and means of utilizing this phenomenon into project management. This research aims to find AI methods, tools, approaches, models and frameworks for each of the project management knowledge domains introduced by PMboK. The methodology followed the PRISMA guidelines for systematic literature reviews to collect, screen and analyze the literature to find relevant studies. The findings presented bibliographic data on the topic and current trends, frequently used AI methods and project management techniques and tools which benefit from these AI methods under each project management domain.

This study presents a comprehensive framework for Project Risk Management (PRM), tailored specifically for Engineering, Procurement, and Construction (EPC) projects. Addressing gaps in traditional risk assessment methodologies, the proposed approach integrates advanced techniques for risk identification, analysis, and evaluation based on risk characteristics. A three-stage framework is proposed utilizing the Delphi method for risk identification and contextualization of risks, the risk analysis stage employs the Fuzzy Level-Based Weight Assessment (F-LBWA) method to achieve fuzzy weights for risk characteristics which the risks will be evaluated by. The final evaluation stage uses the Fuzzy Combined Compromise Solution (F-CoCoSo) method to rank risks, categorizing them as threats, opportunities, or hybrids. A case study of an EPC project demonstrates the framework’s practical application, highlighting construction-phase risks as the most critical threats (negative risks) while also emphasizing opportunities (positive risks) which can be exploited. By incorporating fuzzy logic and innovative Multi-Criteria Decision-Making (MCDM) methods, the framework provides a flexible and robust tool for modern PRM.