The rapid advancement of edge computing, 5G networks, and cloud security is transforming the cybersecurity landscape, enabling real-time data processing, enhanced connectivity, and scalable digital infrastructure. However, the convergence of these technologies also introduces new vulnerabilities, particularly in protecting critical infrastructure in the United States. As edge computing decentralizes data processing, it expands the attack surface, exposing systems to DDoS attacks, data breaches, and AI-driven cyber intrusions. Meanwhile, the low-latency architecture of 5G increases risks related to network slicing security, supply chain threats, and unauthorized access. Cloud security, while essential for ensuring data confidentiality and resilience, remains vulnerable to misconfigurations, insider threats, and evolving attack vectors. This paper proposes an integrated AI-blockchain security framework to mitigate these challenges. AI-powered threat intelligence and anomaly detection models are deployed at the edge, providing real-time defense mechanisms against cyber threats. Blockchain-based authentication enhances identity verification and access control, ensuring data integrity and preventing tampering. In addition, federated learning enables decentralized threat intelligence sharing, improving cybersecurity collaboration across cloud environments. Regulatory measures such as CISA’s national cybersecurity directives and NIST’s 5G security framework are evaluated to support a comprehensive risk mitigation strategy. This study concludes that a synergistic security approach, combining AI-driven threat detection, blockchain security, and federated learning, is essential for strengthening USA’s critical infrastructure resilience and mitigating emerging cyber threats in an increasingly interconnected digital ecosystem.