The increasing demand for secure and reliable wireless communication has driven the development of advanced techniques for channel prediction and estimation. Due to the shift in research knowledge to artificial intelligence (AI). The AI-based channel prediction has emerged as a promising solution to enhance security and performance in wireless networks. This paper explores AI-driven approaches for predicting wireless channel characteristics, enabling proactive countermeasures against eavesdropping, jamming, and unauthorized access. Machine learning (ML) and deep learning (DL) models are employed to analyze real-time channel variations and optimize resource allocation. By leveraging AI-based prediction techniques, wireless networks can adapt dynamically to environmental changes, ensuring enhanced security, reduced latency, increase throughput and improved communication efficiency. This research highlights the application of AI into wireless protocols to develop intelligent and secure wireless communication frameworks. The proposed approach is evaluated through simulations and real-world scenarios, demonstrating its effectiveness in mitigating security threats while optimizing network performance.