In today’s hyperconnected digital ecosystem, where cyberattacks are increasingly sophisticated and infrastructure complexity continues to rise, conventional static defense systems are no longer adequate. Enterprises are now shifting toward AI-augmented cyber resilience frameworks that proactively anticipate, adapt to, and recover from threats. This paper explores the integration of artificial intelligence (AI) into predictive threat modeling within software-defined networks (SDNs) and cloud-native infrastructures, highlighting its transformative role in redefining resilience at both the network control and application layers. The study begins with a broad analysis of evolving cyber threats, emphasizing their polymorphic nature and the challenges of securing decentralized architectures. It then narrows its focus to the application of machine learning and neural networks in identifying anomalous patterns, automating response protocols, and dynamically segmenting threat zones within programmable SDN layers. In parallel, AI models are evaluated for their role in managing the ephemeral, containerized environments typical of Kubernetes and serverless cloud-native deployments, where traditional security controls struggle to maintain visibility and policy enforcement. A multi-layered resilience architecture is proposed, integrating AI-driven telemetry analysis, intent-based security orchestration, and continuous compliance auditing. Special attention is given to edge intelligence and real-time inference for distributed denial-of-service (DDoS) prevention, insider threat detection, and lateral movement containment. By harmonizing AI with zero-trust principles and SDN control logic, organizations can create adaptive frameworks that not only withstand but also learn from cyber incidents. Ultimately, this work demonstrates how AI-augmented frameworks provide a future-proofed foundation for predictive, scalable, and context-aware cyber resilience essential for securing next-generation digital infrastructures across dynamic and virtualized domains.