Cloud-fog computing has emerged as the contemporary approach for processing and analyzing Internet of Things applications due to its ability to offer remote resources. Cloud fog computing technology provides shared resources, information, and software packages, supporting distributed parallel systems in an open environment. It constructs and manages virtual machines to enhance efficiency and attractiveness. We have consistently strived to tackle challenges affecting the efficiency of cloud fog computing, including ineffective resource utilization and response times. The improvement of these challenges can be achieved through effective task scheduling and load balancing between Virtual Machines, this problem considered as NP-hard problem. This paper proposes a Hybrid K-means Clustering Honey Bee algorithm (HKCHB) to cluster Virtual Machines into two or more clusters. Subsequently, the hybrid Honey Bee algorithm is employed for task scheduling, enhancing load balance performance. The proposed algorithm is compared with other task scheduling and load balancing algorithms, including Round Robin, Ant Colony, Honey Bee, and Particle Swarm Optimization Algorithm, utilizing the CloudSim Simulator. The results demonstrate the superiority of the proposed algorithm, yielding the lowest response time. Specifically, the response time is reduced by 22.1%, and processing time is reduced by 47.9%, while throughput is increased by 95.4%. These improvements are observed under the assumption of multiple tasks in a heterogeneous environment, utilizing one or two Data Centers with Virtual Machines. This contribution gives the impression that network systems based on the Internet of Things and cloud fog computing will be improved in the future to operate within the framework of real-time systems with high efficiency.