Predictive cloud resource management: Developing ml models for accurately predicting workload demands (CPU, memory, network, storage) to enable proactive auto-scaling. AI-driven instance type selection and rightsizing. predicting spot instance interr

The modern information technology revolution brought about by cloud computing affects how organizations handle their infrastructure provisioning along with scaling and management. Yet, these organizations continuously fight to maximize cloud resource utilization. Systems that either provide excessive capacity or insufficient resources face the problem of increased expenses together with potential performance deterioration. The paper explores the creation and deployment of machine learning (ML) models that precisely forecast cloud workload requirements for proactive resource management systems. Applications that use workload forecasts to drive auto-scaling improve both elasticity and delay performance. AI systems are also applied to choose instance-type configurations that maintain cost-effective operational alignment with workload patterns. The main objective is to detect spot instance interruptions because these unpredictable disruptions cause problems with critical workloads. The research implements classification alongside time-series models to identify when interruptions occur before taking proactive measures for their mitigation. The paper examines advanced forecasting techniques for cloud spending to enable better financial governance and improved budget planning for organizations. Predictive ML models used with cloud resource management frameworks have established themselves as critical elements that enhance cloud operation efficiency through improved resilience and better cost control. This approach bridges data intelligence with adaptive infrastructure methods and intelligent cloud operations within the current digital transformation environment.