Artificial Intelligence (AI) is revolutionizing DevOps and Kubernetes infrastructure automation by introducing predictive scaling and self-healing architectures. This research explores how AI-driven automation enhances DevOps efficiency, optimizes cloud infrastructure, and reduces manual intervention in Kubernetes-based environments. The study investigates the integration of AI models for anomaly detection, dynamic resource allocation, and real-time failure recovery, leading to improved system resilience and cost-efficiency. We present a comparative analysis of AI-powered predictive scaling models and traditional scaling mechanisms, demonstrating significant improvements in response time, resource utilization, and incident resolution. Additionally, the paper evaluates self-healing mechanisms that leverage AI to detect and remediate failures autonomously. Experimental results validate the efficacy of AI-driven Kubernetes automation in optimizing deployment cycles, minimizing downtime, and enhancing system reliability. The findings contribute to the evolution of DevOps practices by bridging AI with cloud-native technologies for seamless automation and efficiency.