E-commerce's explosive expansion calls for more flexible approaches to inventory control, pricing, and predicting consumer behavior. A cloud-powered hybrid framework that combines RKM for noise-tolerant clustering, HMM for sequence prediction, QMC for customer behavior simulation, and Cuckoo Search for optimization is proposed in this paper. Cloud computing guarantees scalability and quick data processing. Over 92% of demand forecasts, 91% of resource allocations, and 93% of consumer behavior predictions were accurate, surpassing the accuracy of conventional methods. The hybrid method enhanced customer satisfaction, operational efficiency, and decision analytics through dynamic adaptation to market changes. Inventory management, pricing tactics, and personalization were all improved by its scalable design, which successfully solved issues in digital markets. With the help of cloud infrastructure and a variety of algorithms, this framework offers a creative, effective way to satisfy the changing needs of Modern E-Commerence.