Cloud computing is the most emerging technology in the era that provides scalable, distributed, elastic computer resources for the end-user using the internet.However, the Cloud Computing Networks(CCNs) fails in providing many Quality of Services(QoS) such as reliability, load balancing, resource allocation and task scheduling, security, etc out of which the most challenging task in cloud computing is task scheduling, which directly affects the operational cost and resource usage of the system.Hence,this paper proposes an advanced technique named the Wolf Hunting Approach (WHA)for further improvement of an optimal solution for searching with a multi-objective optimization technique. The implementation of WHA involves simulation based experiment and shows that WHA has better accuracy and convergence speed in seeking for optimal task scheduling. In comparison to the current meta-heuristic algorithms, it provides superior performance in utilization of system resources in the presence of large-scale and small-scale tasks. The approach is well illustrated with suitable examples