The rapid evolution of wireless communication technologies has led to the emergence of 6G networks, which demand highly adaptive and intelligent resource allocation strategies to enhance efficiency and reliability. This paper proposes a fuzzy logic-based adaptive resource allocation framework to optimize spectrum utilization, energy efficiency, and network latency in dynamic 6G environments. Unlike conventional resource allocation techniques that rely on rigid mathematical models, fuzzy logic enables flexible, human-like decision-making by incorporating uncertainty and imprecision in network conditions. The proposed model considers key input parameters such as network congestion, user demand, signal quality, and energy consumption, which are processed using fuzzy inference rules to dynamically allocate network resources. MATLAB’s Fuzzy Toolbox is used for simulation, and the results demonstrate that the fuzzy logic-based approach improves network efficiency by up to 25% compared to traditional methods, ensuring seamless connectivity and lower latency. This study highlights the potential of fuzzy logic in enhancing autonomous decision-making in 6G networks and provides a foundation for future research in intelligent wireless resource management.