Artificial intelligence has revolutionized threat detection and risk management in the evolving cybersecurity landscape. By combining machine learning and deep learning techniques, proactive cybersecurity strategies can be developed. AI models can predict cyber threats early by analyzing various data sources like network logs and system activity records. Integration of ML algorithms with advanced data analysis techniques such as clustering provides a comprehensive view of security incidents. Hybrid models like convolutional neural networks and recurrent neural networks, along with transfer learning and explainable AI, enhance anomaly detection capabilities. These AI-driven approaches improve cyber resilience by detecting threats early on and offering adaptive responses to protect critical assets and maintain operational continuity.