The growing digitalization of power systems and industrial control environments has heightened both their efficiency and vulnerability, making cybersecurity a central concern for modern energy delivery networks. Smart grids, supported by cloud-based communication and control infrastructures, face escalating risks from cyberattacks that target supervisory control and data acquisition (SCADA) systems, distributed energy resources, and grid communication protocols. These threats jeopardize not only service continuity but also national security and economic stability. Addressing this dual imperative of resilience and protection requires advanced frameworks that combine adaptive intelligence with scalable cloud architectures. This paper introduces an AI-enhanced cloud networking framework tailored for securing smart grids and critical infrastructures. At a broad level, the approach leverages cloud-based scalability and distributed data processing to monitor grid components and industrial systems in real time. Within this architecture, deep packet inspection provides granular visibility into communication flows, anomaly detection algorithms identify irregular patterns that signal potential breaches, and predictive AI models anticipate emerging attack vectors before they materialize. By fusing these layers, the framework ensures proactive threat identification and rapid containment of malicious activity. Applied specifically to industrial control systems and energy distribution networks, the model demonstrates how AI-driven analytics can harden infrastructure resilience by minimizing downtime, securing data flows, and ensuring operational integrity. Narrowing the focus, the framework also highlights its ability to scale across diverse energy ecosystems, adapting to heterogeneous infrastructures without sacrificing performance. By uniting cybersecurity and resilience, this work contributes a forward-looking pathway to safeguard critical national assets in the era of intelligent, cloud-integrated energy systems.