This research examines how effective the Weighted Round Robin (WRR) algorithm is compared to the Time-Based Round Robin algorithm in improving the efficiency of cloud monitoring systems. WRR adjusts the allocation of monitoring resources according to workload priorities, whereas Time-Based Round Robin allocates resources in a cyclical manner. The aim of the research is to examine how they affect the use of resources and the ability to respond in the context of monitoring cloud systems. Introduction: The monitoring of cloud systems is essential for maintaining peak performance and efficient use of resources in cloud environments. The effectiveness of monitoring and resource allocation is greatly impacted by the selection of a scheduling algorithm. Materials and Methods: In this study, simulations are used to assess how the Weighted Round Robin and Time-Based Round Robin algorithms perform in cloud monitoring systems. The efficiency enhancement of each algorithm is assessed by evaluating metrics like resource utilization, responsiveness, and scalability. Findings: The results show that the Weighted Round Robin algorithm performs better than the Time-Based Round Robin algorithm in terms of using resources and being responsive. By adapting monitoring priorities according to the workload, WRR ensures more efficient allocation of resources and quicker response to urgent events. In conclusion, the results indicate that the Weighted Round Robin algorithm provides substantial improvements in efficiency for cloud monitoring systems when compared to the Time-Based Round Robin algorithm. WRR improves overall system efficiency by dynamically allocating resources according to workload priorities, ensuring optimal performance and responsiveness.