Cognitive Radio Ad hoc Networks (CRAHNs) have emerged as a promising solution to address the spectrum scarcity problem by allowing unlicensed secondary users to opportunistically access underutilized spectrum bands. However, efficient and dynamic spectrum access in CRAHNs remains a challenging task due to the dynamic nature of the network and the unpredictable spectrum availability. This paper proposes an AI-based scheduling protocol for Cognitive Radio Ad hoc Networks (AI-SCAN) to address the spectrum access problem in CRAHNs. The protocol utilizes machine learning techniques to enable intelligent decision-making for spectrum allocation and scheduling. Simulations are conducted to evaluate the performance of AI-SCAN in comparison to existing scheduling protocols. The results demonstrate that AI-SCAN achieves superior performance in terms of spectrum utilization, network throughput, and fairness among secondary users. The protocol effectively balances the trade-off between maximizing spectrum utilization and minimizing interference, thereby enhancing the overall efficiency and reliability of CRAHNs.