AI-powered data warehousing represents a significant evolution in cloud storage technologies, reshaping the way organizations handle, analyze, and extract insights from their growing data volumes. As data generation accelerates across a variety of industries, traditional on-premises storage solutions are proving inadequate to meet the growing demands for scalability, high-speed processing, and intelligent data management. Combining Cloud-based machine learning (ML) and artificial intelligence (AI) warehousing addresses these challenges by unlocking powerful capabilities such as real-time analytics, predictive insights, and automated data manipulation.This study explores the transformative role of AI in cloud storage, focusing on key performance indicators such as data volume, query latency, AI optimization, redundancy strategies, and compression performance. Using advanced Machine learning techniques, especially random forest, decision tree regression, and XG boost regression– we evaluated the performance of cloud storage systems under various configurations. Our analysis confirms that AI-integrated solutions outperform conventional methods, especially in optimiz
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