Distributed computing gives a backing to buyers to diminish their inner foundation, and for suppliers to expand incomes, utilizing their own particular framework. The proper load balancing and dynamic resource provisioning improves cloud performance and attracts the cloud users. In this paper, we propose an automated resource provisioning algorithm, Speculation resource provisioning, prompting load balancing through speculative approach in resource provisioning. As an attempt to quantify resource allocation we use two level adaptive prediction mechanism to check the computational patterns of past resource allocation to the future requirement. The framework guarantees suitable resources required for the application, by dodging over or under-provisioning of resource and supports energy-efficiency in resource allocation. We use estimation methodology to address the variability in the historical data to balance the speculation overhead. We have conveyed our proposed work in an open source cloud structure and contrasted our outcomes and other machine learning approaches. Our Experimental results demonstrate adaptive resource allocation over customer-driven service management under the rapidly changing requirements of cloud computing.