Big data research for health service applications, from the approval of the cloud computing and the Internet of Things (IoT) model of healthcare brought drastic changes in the medical field and improved healthcare services. But the required source to bring the data in a cloud-IoT environment poses a big challenge. In this research work, optimization techniques of virtual machines (VMs) in cloud environment are introduced. The performance of healthcare methods by decreasing the stakeholder’s requirements, execution time, CPU utilization, and storing the patient’s digitals is considered in this research work. The proposed structure defines different steps such as customer devices, customer request (tasks), cloud broker, and network administrator. Three optimization methods such as the Cuckoo Search Algorithm, Particle Swarm Optimization, and Artificial Bee Colony Optimization (ABCO) are employed in the research to optimize the execution time of the stakeholder’s request. The fitness function consists of three fields such as CPU utilization, turn-around time, and waiting time. The experimental result shows the details about three optimization techniques in order to enhance execution time, data processing time, and system efficiency. The simulation result shows that the proposed method decreases the performance rate of total execution time and the system efficiency regarding the real-time improvement of the system. After comparing the proposed optimization methods, ABCO achieves a better efficiency rate of 92.5%, for use in industries.