Ensuring cybersecurity in critical frames such as healthcare, finance, and national defense is vital due to the growing complexity of digital ecosystems and the evolving nature of Conventional digital security risks. Interference detection there is often a struggle for systems to detect novel attacks and restrained anomalies. “To deal with this challenge, this paper suggests an original hybrid AI ideal for anomaly detection and threat prediction through unsupervised machine learning techniques. The model shows advanced feature selection with clustering methods and optimizes performance through the Whale Optimization Algorithm and neural networks. Additionally, a combination of the Random Forest method and the Grasshopper Optimization algorithm is used to increase clustering accuracy and detection precision. The projected strategy provides a more resilient and flexible cyber security framework by outperforming conventional methods in spotting hidden threats and anomalous trends. This hybrid strategy strengthens threat mitigation and enhances resilience in modern cyber environments.