With the advancement of technology, the speed and efficiency of information processing have become vital for meeting the growing demands of individuals and organizations. As time constraints increase, rapid and accurate access to information has gained critical importance. To address these challenges, organizations in the business and public sectors are increasingly relying on simulation methods, a core area of computer science, to optimize their responses to customer demands. Simulation provides a robust framework for analyzing and modeling complex systems. Within this framework, queue theory plays a central role by examining how systems handle incoming demands and offering insights into improving resource utilization, minimizing waiting times, and enhancing overall performance, particularly in service industries. This study provides a detailed analysis of queue theory, exploring its fundamental principles, key features, and various models. Additionally, a comparative evaluation of different queueing models is conducted through simulation, assessing key performance metrics such as server utilization, maximum queue length, and average response time. The results indicate that model selection significantly impacts system efficiency, with certain models exhibiting superior performance under specific conditions. These insights equip organizations with the tools to develop more effective strategies, optimize their processes, and enhance responsiveness to evolving demands.