<p>The physical world is ready for ubiquitous integration with Internet of Things (IoT) systems in the next generation of residential and metropolitan settings. Intelligent IoT technology deployments and their applications are increasing at an unprecedented rate. Most of these technological applications revolve around gathering, storing, and sharing user-generated data. However, these IoT applications have significant limitations in their capacity to operate, including data storage, energy usage, and processing power. The combination of IoT infrastructure and cloud computing represents the most feasible way of handling the huge amounts of available information and creating value-added services on top of that. Cloud systems can offer IoT networks nearly unlimited possibilities in, for example, information storage, processing power, and data analysis. The current state of technology sees cloud and IoT capabilities maturing together into a coherent cloud-enhanced IoT ecosystem. The selection and combination of discrete services into unified solutions addressing client requirements demand increasing attention. The growing user preference for cloud-integrated IoT solutions is luring service providers to come up with offerings that have varied operational and peripheral attributes. As a result, service integration challenges and effective resource allocation issues have emerged as critical concerns under the cloud-based IoT ecosystem. In this study, a novel model is designed to measure the success of service composition approaches that employ an enhanced Swarm-based Optimization algorithm. Coupling Particle Swarm Optimization Algorithm (PSO) and Gravitational Search Algorithm (GSA) algorithms may yield an effective approach to optimizing service composition in cloud information systems. This hybrid method, which uses GSA’s deep exploration ability and PSO’s rapid convergence, can be used to efficiently optimize cloud service composition. This method will enhance the quality of services, decrease the computational expense, and improve the processing efficiency. Experimental results show that the proposed method is better than conventional methods with regard to optimizing the most significant metrics and can be employed as an effective framework for service composition evaluation and improvement in cloud computing.</p>