By implementing machine learning algorithms in the AWS Cost and Usage Report data set, the study efficiently solves the problem of cloud resource management cost optimization. With the removal of wasteful operational costs, the study discusses cloud resource usage inefficiencies and formulates an AI-based methodology for resource demand prediction and optimization. The study estimates an average cost reduction of 18% using the optimization model, CPU cost reduction of 20%, memory cost reduction of 18.75%, and storage cost reduction of 16%. The model further indicates that CPU, memory, and storage resource availability were increased by 7%, 10%, and 15%, respectively. The study also illustrates 15% greater utilization by optimized resource allocation, equating to fewer virtual machines. The study proves that AI-optimized methods can reduce the cloud cost by half without impacting performance. It is an economical cloud cost optimizer for business users seeking the best value for money spent on cloud services.