Cognitive Radio Networks (CRNs) have emerged as a promising solution to enhance the efficiency and reliability of communication systems in flood management scenarios. Floods are natural disasters that can cause extensive damage to infrastructure and pose significant risks to human lives. Effective communication during such events is critical for timely response and mitigation efforts. CRNs, with their ability to adapt to dynamic spectrum availability, offer a robust communication infrastructure for flood management systems. This research explores various routing approaches tailored for CRNs within the context of flood management. The primary goal is to ensure uninterrupted communication among various stakeholders, including emergency responders, authorities, and affected communities, even in the face of spectrum scarcity and interference. We present a comprehensive analysis of routing strategies that leverage cognitive radio technology to optimize communication in flood-prone areas. The proposed routing approaches include spectrum-aware routing, channel assignment algorithms, and dynamic spectrum access techniques. These strategies enable CRNs to dynamically select available spectrum bands, minimize interference, and route data efficiently. Moreover, we investigate the integration of machine learning and artificial intelligence algorithms to predict flood dynamics and adapt routing decisions accordingly.