This paper investigates a novel approach to Quantum Key Distribution (QKD) enhanced by Quantum Projective Simulation (QPS), a quantum-inspired reinforcement learning model. By allowing agents to simulate future actions through quantum random walks over a memory network of quantum clips, QPS enables Alice and Bob to dynamically adapt their key distribution strategies based on past experiences. Simulation results show that the QPS-based QKD model achieves faster convergence to optimal strategies, improves resilience against eavesdropping attacks, and maintains a higher success probability compared to traditional QKD methods. These findings suggest that integrating QPS into QKD can significantly enhance the efficiency and security of quantum communication systems in dynamic and adversarial environments, positioning it as a promising candidate for future 6G networks. Index Terms - QKD, QPS, Quantum Random Walk, Artificial Intelligence.