Abstract—Spectrum scarcity has become a pressing issue due to the rapid growth of wireless devices and increasing demand for data. Cognitive Radio Networks (CRNs) offer a promising solution by allowing dynamic spectrum access, which depends on efficient handoff decisions by Secondary Users (SUs). Accurate handoff decisions are vital to prevent ping-pong effects and interference that could disrupt Primary Users (PUs). In this study, we propose a fuzzy system with adaptive Membership Functions (MFs) to improve handoff decisions in CRNs. Our methodology involved simulating three models (Fuzzy Hybrid, Fuzzy MF Optimization with Particle Swarm Optimization (PSO), and the proposed adaptive MF model) using ten diverse test datasets representing different network conditions. We measured performance by comparing handoff occurrences across the models. Results show that handoff frequency was reduced from 50% in the Fuzzy Hybrid model to 40% with PSO optimization and further to 30% using adaptive membership functions. The adaptive model achieved this reduction through more precise handoff decisions, demonstrating its effectiveness in reducing unnecessary handoff occurrences in CRNs.