The cloud users are rising in recent years due to their advanced services and benefits which increases the growth of business. As the users are increasing the tasks in the cloud environments is also evolving which is delaying the task scheduling performance that affected the transmission of data. Different types of models aimed to enhance the performance of task scheduling but failed to get the optimal solution for task scheduling due to overloaded task allocation. In this research, the Degree Day Function-Snow Ablation Optimization (DDF-SAO) approach is proposed to find the optimal result for task scheduling that balance the load in cloud atmosphere. The performance of the DDF-SAO approach is evaluated based on the existing performance metrics called throughput, makespan and resource utilization. The developed DDF-SAO model attained minimal makespan and resource utilization of 13.78 s and 39%, respectively. The proposed DDF-SAO approach has also obtained higher throughput of 124 s compared to the existing optimization methods used for process of task scheduling.