Internet of Things (IoT) and cloud computing are the expertise captivating the technology. The most astonishing thing is their interdependence. IoT deals with the production of an additional amount of information that requires transmission of data, storage, and huge infrastructural processing power, posing a solemn delinquent. This is where cloud computing fits into the scenario. Cloud computing can be treated as the utility factor nowadays and can be used by pay as you go manner. As a cloud is a multi-tenant approach, and the resources will be used by multiple users. The cloud resources are required to be monitored, maintained, and configured and set-up as per the need of the end-users. This paper describes the mechanisms for monitoring by using the concept of reinforcement learning and prediction of the cloud resources, which forms the critical parts of cloud expertise in support of controlling and evolution of the IT resources and has been implemented using LSTM. The resource management system coordinates the IT resources among the cloud provider and the end users; accordingly, multiple instances can be created and managed as per the demand and availability of the support in terms of resources. The proper utilization of the resources will generate revenues to the provider and also increases the trust factor of the provider of cloud services. For experimental analysis, four parameters have been used i.e. CPU utilization, disk read/write throughput and memory utilization. The scope of this research paper is to manage the Cloud Computing resources during the peak time and avoid the conditions of the over and under-provisioning proactively.