Optimizing Kubernetes workloads with AI-driven performance tuning in AWS EKS

The rush toward Kubernetes adoption in cloud environments requires next-level methods to enhance workload optimization because of its popularity. The scalability of AWS Elastic Kubernetes Service (EKS) receives benefits from its infrastructure while traditional performance optimization schemes that utilize static thresholds and configuration parameters result in insufficient resource distribution and delayed operations and increased spending. This research evaluates how artificial intelligence supports Kubernetes workload optimization through artificial intelligence-driven predictions and anomaly monitoring and intelligent queuing mechanisms as well as constant performance optimization. AI analysis of historical and real-time data through automated techniques controls resource scaling to improve system reliability and minimize performance impediments. The studied AI system enables better CPU and memory efficiency alongside lower cloud bills and faster performance through automated resource changes that align resources with workload requirements. AI detection of anomalies enables systems to become more resistant to operational disruptions because it identifies impending failures before operational impact occurs. Kubernetes workload management in AWS Elastic Kubernetes Service brings organizations enhanced performance along with financial efficiency alongside reliable system operation characteristics. AI optimization will advance cloud-native operations by delivering automated self-healing systems with enhanced cost-efficiency according to predictions.