The composition of electronic government (E-government) cloud services is essential for optimizing resource allocation, load balancing, and energy efficiency, thereby ensuring seamless digital service delivery. optimizing cloud service composition remains a massive challenge because of dynamic workloads and varying aid needs. This research investigates the implementation of Particle Swarm Optimization (PSO) to improve the cloud service composition in egovernment platforms by effectively distributing workloads, minimizing execution time, enhancing Virtual Machine (VM) usage, and decreasing energy consumption. The performance of the proposed PSO-based technique was evaluated using CloudSim 5.0, and the results were compared with the Ant Colony Optimization (ACO) algorithm across key performance metrics, such as execution The composition of electronic government (E-government) cloud services is essential for optimizing resource allocation, load balancing, and energy efficiency, thereby ensuring seamless digital service delivery. optimizing cloud service composition remains a massive challenge because of dynamic workloads and varying aid needs. This research investigates the implementation of Particle Swarm Optimization (PSO) to improve the cloud service composition in egovernment platforms by effectively distributing workloads, minimizing execution time, enhancing Virtual Machine (VM) usage, and decreasing energy consumption. The performance of the proposed PSO-based technique was evaluated using CloudSim 5.0, and the results were compared with the Ant Colony Optimization (ACO) algorithm across key performance metrics, such as execution​