Zero-click attacks represent a significant and continuously evolving cybersecurity threat, infiltrating digital devices silently without requiring user interaction. This paper provides a comprehensive review of zero-click exploit mechanisms, predominant attack vectors, and existing strategies for their mitigation. It critically examines anomaly detection approaches, including machine learning techniques such as behavioral analytics based on dynamic moving averages, historical traffic analysis, and the role of blockchain technology in establishing secure and tamper-proof logging systems. By leveraging these advanced analytical techniques, our approach enhances the detection of subtle network anomalies indicative of zero-click exploits. Additionally, integrating threat intelligence into the framework significantly improves its contextual awareness and responsiveness to emerging cybersecurity threats. Furthermore, the paper highlights existing gaps and challenges in current methodologies, emphasizing the necessity of continual refinement and adaptation in detection strategies. Ultimately, the paper underscores the importance of an integrated cybersecurity defense strategy, combining advanced data analytics and robust, adaptive security measures to effectively counteract the sophisticated and evolving risks posed by zero-click exploits.