aspg-3-2096-1791943558 20261014020558 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2026 13 1 Maryam Ghassan Majeed Technical Computer Engineering Department, Al-Kunooze University College, Basrah, Iraq Waleed Hameed Medical instruments engineering techniques, Al-farahidi University, Baghdad, Iraq Noor Hanoon Haroon Department of Computer Technical Engineering, Technical Engineering College, Al-Ayen University, Thi- Qar, Iraq Sahar R. Abdul Kadeem Department of Medical Devices Engineering Technologies, National University of Science and Technology, Dhi Qar, Nasiriyah, Iraq Hayder Mahmood Salman Computer Technologies Engineering, Al-Turath University College, Baghdad, Iraq Seifedine Kadry Department of Applied Data Science, Noroff University College, Kristiansand, Norway; Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, 346, United Arab Emirates; Depart Resource Management in Physical Education (RMPE) is the term used to describe the management of the curriculum, materials, and human resources needed for Physical Education (PE). Due to increased sports and physical activity participation, student performance in PE classes across all schools and universities has decreased. According to the analysis, it is hard for the available PE educators and managers to establish a relationship between all the resources. This study uses a robotic system with 5G capability for RMPE. The Big Data Analytics-based Artificial Neural Network method (BDA-ANNA) handles all PE resources in this computerized system. The BDA-ANNA can efficiently increase RMPE work quality and efficiency, enabling managers to obtain and save appropriate information accurately and quickly. With assistance from the robotic system, the material stock may be maintained. With the aid of BDA-ANNA, the mechanical system can keep the material stored. Automated systems with 5G capabilities can provide PE instructors with complete remote-control access with a 2-millisecond latency. These two clauses mandate that the RMPE supervise athletic events and physical activity. The suggested 5 G-enabled robotic systems for RMPE can manage all the resources effectively and efficiently with a low error rate. The advanced system and BDA-ANNA were put through a simulation exercise, demonstrating their independence in classifying and managing resources while reducing processing time. The experimental result improves a prediction ratio of 95.5 %, a learning ratio of 90.5%, an error rate of 92.3%, an Efficiency ratio of 96.6%, an Accuracy ratio of 92.5%, and performance ratio of 96.7%, a Movement Detection ratio of 90.7% compared to other methods. 2026 162 174 2096 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.130113 https://americaspg.com/journal/3/article/2096 https://americaspg.com/storage/11695066765.pdf