Mobile cloud computing is used utility based computing, where services are accessible over the internet through mobile devices, laptop, and other portable devices. In this generation where most applications run on the cloud, the quality of service (QoS) is an inherent element of service-oriented architecture. The performance of cloud service may change due to dynamic environments. The services are measured by the QoS parameters which are intrinsically uncertain. The growth of services on the internet, identifying the best services is very challenging in an economical manner. This paper proposed efficient QoS-aware cloud service ranking (CSR) method by using support vector machine (SVM). The CSR method decides a service ranking by using QoS parameters. The experimental results show the train model accuracy of the dataset. This algorithm takes O(n) time for cloud service ranking and its performance is better than conventional methods like AHP, ANP, and TOPSIS. © 2017 Inderscience Enterprises Ltd.