Cloud computing has been developed and become the foundation of a wide range of applications. It is a distributed computing model which enables developers to automatically deploy their applications onto the cloud. Task scheduling plays a vital role in the function and performance of the cloud computing system. While there exist many approaches for improving task scheduling in the cloud, it is still an open issue. This paper presents, Workload Prediction Framework for Task scheduling in Cloud Computing. Task scheduling algorithms can be designed for static or dynamic scenarios. The aim of the proposed system is to improve resource utilization & response time in the cloud using scheduling algorithms. Rather than implementing single scheduling algorithms, multiple scheduling algorithms are implemented. Selection of the efficient scheduling algorithm is done using machine learning classification. Based on classification rules efficient scheduling algorithm is selected and tasks are executed. Task scheduling can consider different parameters for scheduling purposes like Makespan, QoS, energy consumption, execution time, and load balancing. The outcome of the proposed work leads to the selection of the best task scheduling algorithm for the input task (request).