<p>Spectrum sensing is critical in Cognitive Radio Networks (CRNs) for optimizing spectrum utilization and improving wireless communication efficiency. Effective spectrum sensing is crucial for reducing interference, enhancing energy efficiency, and ensuring optimal resource allocation as the demand for dynamic spectrum access increases. This study analyzes various advanced spectrum sensing approaches designed to address the key challenges in CRNs. It focuses on techniques meant to increase detection precision, lower false alarms, and maximize energy use, all enhancing CRN performance in general. The paper assesses these methods’ efficacy in various network scenarios, emphasizing their capacity to provide high-quality spectrum sensing. This study’s findings demonstrate significant improvements in detection probability and energy efficiency, with a marked reduction in false alarms and interference. Simulations reveal that these approaches can effectively manage real-time spectrum, providing enhanced network coverage and reduced spectrum congestion. The predictive results further validate the proposed approach, showcasing its ability to optimize spectrum sensing while maintaining high accuracy and stability across varying network conditions. The model demonstrates rapid convergence, efficient learning, and superior classification performance, ensuring robust detection of spectrum opportunities even in challenging environments. The findings provide insightful information on how these cutting-edge techniques might aid in the creation of more reliable and effective CRNs for next-generation wireless communication networks.</p>