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.

Projects are essential mediums for businesses, organizations and industries to reach their strategic goal, however managing multiple projects simultaneously is a complex task. There have been a number of studies conducted on developing and introducing frameworks and models to help optimize project portfolio management. This study aims to identify these frameworks and analyze them to gain an understanding of the core components of these frameworks and to introduce the recent models and approaches used to optimize project portfolios. The methodology followed the guidelines of systematic literature reviews, by identifying a substantial number of papers, screening them and analyzing them until the research goals were met. The findings included 46 of relevant articles proposing models in project portfolio context, the data of the articles were recorded and presented and the models proposed were analyzed. Bibliographical findings were provided and by analyzing the models their core components were identified and using the core components a figure presenting the key components was illustrated.