As a potential paradigm for enabling effective and low-latency computation at the network’s edge, edge computing has recently come into the spotlight. In edge computing environments, resource allocation is essential for ensuring the best possible resource utilization while still satisfying application requirements. Traditional resource allocation algorithms, however, struggle to effectively capture the uncertainties and ambiguity associated with resource availability and application needs because of the dynamic and varied nature of edge environments. This research offers a fuzzy logic-based method for planning to allocate resources in edge computing. Fuzzy logic offers a flexible and understandable framework for modeling and reasoning with imperfect and ambiguous data. The suggested method offers a more reliable and adaptable resource allocation system that can successfully address the uncertainties present in edge computing by utilizing fuzzy logic. The resource allocation process incorporates fuzzy membership functions to capture the vagueness of resource availability and application requirements. Fuzzy rules are defined to map the linguistic variables representing resource availability, application demands, and performance objectives to appropriate resource allocation decisions. The fuzzy inference engine then utilizes these rules to make intelligent decisions regarding resource allocation, considering the fuzzy inputs and the system’s predefined objectives.