Cloud computing is a rapidly evolving field that is widely considered the third major revolution in IT, following advancements in computer technology and the Internet. This innovative approach allows service providers to offer a wide range of resources, such as computing power, storage capacity, and software applications, to customers at a cost-effective price point. As the customer base grows, addressing their needs effectively can become a significant and challenging concern. The allocation of resources emerges as a critical factor, often limited by the company's available resources. This restriction on resource availability poses a primary challenge for businesses as they strive to meet the increasing demands of their expanding customer base. The issue of resource allocation in cloud computing is viewed as a complex optimization problem for large enterprises due to the substantial number of customers and resources involved. The manta rays optimization algorithm (MROA) specifically addresses this challenge by offering a targeted solution for efficient resource allocation in this context. The objective of the algorithm is to identify an optimal task scheduler for resources, considering various factors such as the overall task execution time, resource allocation, and the quality of service (QoS) for each individual task. The optimization technique utilizes the foraging behavior observed in manta ray chains to discover a well-balanced set of optimized solutions. Experimental results confirm the effectiveness and efficiency of the proposed algorithm.