American Journal of Business and Operations Research AJBOR 2692-2967 2770-0216 10.54216/AJBOR https://www.americaspg.com/journals/show/1683 2018 2018 Faculty of Artificial Intelligence, Data Science department, Egyptian Russian University (ERU), Cairo, Egypt Mahmoud A. Zaher Faculty of Computers and Information, Sadat Academy for Management Sciences, Cairo, Egypt & French University in Cairo, Egypt Nabil M. Eldakhly This paper proposes an integrated machine learning (ML) framework for dynamic resource allocation in a multi-project environment. The framework utilizes machine learning algorithms to predict future resource demands and identify potential resource shortages. The proposed framework considers various factors such as project priorities, resource availability, and project deadlines to optimize resource allocation decisions. The framework is designed to continuously learn from past resource allocation decisions and improve future resource allocation strategies. The effectiveness of the proposed framework is evaluated through a case study in a real-world multi-project environment. The results show that the framework can significantly improve resource utilization and project completion times while reducing resource waste and cost. Overall, the proposed framework provides a practical solution for dynamic resource allocation in complex multi-project environments. 2023 2023 08 15 10.54216/AJBOR.100101 https://www.americaspg.com/articleinfo/1/show/1683