ISSN: 2666-2795 Vol. 7 No.1, 2022, Netherland International Journal of Applied Engineering Research 169 Optimizing Network Performance by Exploration of Resource Management Strategies and Challenges in IoT-Based 6G Networks Saifur Rahman Ansari 1 , Jaffar Ajani 2 , Mohammed Rasheed Hussain 3 , Syed Nayeemuddin Hassan 4 , Mohammed Azim Arif Ansari 5 ansari.saifurrahman@gmail.com, jaffarajani@gmail.com mohd.rashid434@gmail.com, nayeem.sh@gmail.com mohammedazimansari@gmail.com 1 Cloud and Infrastructure Service (CIS), Wipro Arabia Ltd, Riyadh, Saudi Arabia 2 Information Technology, Wipro Arabia Ltd, Al-Khobar, Saudi Arabia 3 Operations, Machinestalk (IoT Solutions), Riyadh, Saudi Arabia 4 Network operations engineer, Xad Technologies LLC (Etisalat), Dubai, UAE 5 Research and Consulting, International Data Corporation (IDC), Riyadh, Saudi Arabia Corresponding author::jaffarajani@gmail.com Abstract: Wireless of communication networks that started with 1G and reached the 5G mark are dramatically altered the means by which societies interact and 6G stands to make the same impact. The 6G age will be marked by huge connection, low latency, and bandwidth capabilities due to the next-generation technologies, including Artificial Intelligence (AI), Terahertz (THz). Nonetheless, the fact that the IoT devices predicted in the 6G environment are of such magnitude and complexity poses some serious challenges on resource management, especially in contexts of energy efficiency, real-time performance, and security. This study explores the resource management measures that should be undertaken to maximize the performance of the IoT devices in the 6G networks. Targeting the sphere of dynamic resources occupation, energy efficiency models, and prospective frameworks, this research will provide an inclusive study of the current strategy with an attempt to fill in the niche in the research so far. This study synthesizes the existing knowledge on IoT resource management in the framework of 6G networks by performing a systematic literature review (SLR) and meta-analysis and suggests a new resource management framework that uses AI, blockchain, and adaptive scaling methods. The framework can be used to cope with the challenges of heterogeneity supported by IoT devices, the real-time necessity of networks, and the energy-efficient operating necessity. The work points to the necessity of combining the resources management systems with AI and blockchain technology to enhance the scalability, safety, and efficiency of the IoT processes in 6G environments as a whole. In the examination of several dynamic resource allocation methods including network slicing, edge computing, and adaptive power control, the study provides recommendations to effective ways of maximizing Quality of Service (QoS) under minimum energy requirements. Besides, the machine learning and reinforcement learning approach are covered as vital predictive models to foresee and react to the changing expectations of 6G networks. Among the most important contributions that the research makes, there is the idea of building a framework that incorporates both real-time data-driven decision-making with protocols that care about security, in the hope to address the performance need of heterogenous IoT applications. The presented framework is likely to offer the effective response to the growing amount of connected devices during 6G and, eventually, enable the ubiquitous usage of IoT technologies in different domains, such as healthcare, smart cities, and industrial automation. The next steps in this area will be related to a more perfected AI algorithms and cross-layer optimization methods as well as the increased level of security maintenance to achieve the sustainability and scalability of 6G networks. The study opens and yields