Scheduling is the process of allocating cloud resources to several users in accordance with a predetermined schedule. Proper parallel operations planning is necessary to achieve good performance in scattered conditions. Work scheduling must take a number of limits and objectives into account in order to create meaningful schedules in the cloud environment. The difficulty of task mapping given the resources at hand is categorised as an NP-hard problem. Cloud computing`s Quality of Service (QoS) problem has to be overcome before it can be deemed successful. Resource allocation is crucial when it comes to tasks with performance Optimization restrictions. The only way to effectively accomplish crucial objectives in cloud computing including high performance, high profit, high utilization, scalability, provision efficiency, and economy is by using an effective task scheduling system. This article suggests a framework based on the Grey Wolf Optimization, Particle Swarm Optimization, and Flower Pollination Algorithms for efficient job scheduling in a cloud computing environment. Job scheduling is done by Grey Wolf Optimization to shorten execution times and lower costs.

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