Cloud computing has become an integral component of modern enterprise infrastructures, offering scalability, flexibility, and operational efficiency. However, rapidly changing workloads, variable pricing models, and inefficient resource utilization often result in unpredictable and excessive cloud expenditure. Traditional rule-based or manual cost management techniques are insufficient for large-scale environments, as they cannot effectively adapt to real-time fluctuations in resource demand or workload behaviour. Artificial Intelligence (AI) provides a transformative approach to cloud cost optimization through intelligent automation and data-driven decision-making. By leveraging machine learning algorithms, AI enhances several critical functions, including demand forecasting, anomaly detection, dynamic workload scheduling, resource rightsizing, and pricing optimization. These capabilities enable organizations to proactively control costs, reduce resource wastage, and maintain high performance across multi-cloud and hybrid cloud environments. This study examines AI-driven frameworks for cloud cost optimization, their mechanisms, benefits, and applicability.It also discusses key challenges such as data privacy concerns, model interpretability, integration complexity, and computational overhead. Additionally, emerging trends—including autonomous cloud optimization, carbon-aware scheduling, serverless intelligence, and edge-enhanced AI are explored to highlight future advancements in the field. The study shows AI can notably improve cost-efficiency, scalability, and reliability in cloud operations.

Future work will validate the proposed framework via real-world case studies and prototype implementations. Through predictive analytics, intelligent auto-scaling, dynamic scheduling, and automated governance, AI significantly reduces unnecessary cloud expenditures while improving overall system performance and reliability. However, challenges such as data privacy, model complexity, vendor lock-in, and the need for continuous monitoring must be carefully addressed. As AI technologies evolve, cloud environments will increasingly transition toward autonomous operations, sustainable resource management, and enhanced security. The integration of AI into cloud infrastructure represents a critical step toward achieving scalable, resilient, and financially optimized digital ecosystems.

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