To resolve the contradictions in edge computing for technology services, such as a long resource scheduling time, an uneven load distribution, and the conflict between the limited resources of edge nodes and the requirements of low latency and high reliability for tasks, in this paper, we propose an edge computing resource-balanced scheduling algorithm for technology services that integrate a genetic–ant colony hybrid algorithm. First, an edge computing scenario is constructed on the basis of cloud distance, and an edge computing network architecture is established. The constraints on the cloud distance of nodes and data transmission rates are clarified, while a task model incorporating task priority classification and an edge computing node model are developed. Second, a multi-objective resource-balanced scheduling model is built by integrating task parameters and scheduling time. A hybrid strategy combining the genetic and ant colony algorithms is adopted to solve the model: the global search capability of the genetic algorithm is used to quickly locate the high-quality solution space, and then the local optimization advantage of the ant colony algorithm is employed to accurately optimize the scheduling scheme, achieving the dual goals of reducing the task execution time and realizing a balanced load distribution across the cluster. Finally, the performance of the algorithm is verified through simulation experiments. The results show that the proposed algorithm can effectively solve the problems of long resource scheduling time and uneven load distribution in edge computing for technology services, significantly reduce system energy consumption, improve system resource utilization, and fully meet the core requirements of low latency and high reliability for edge computing tasks. The algorithm proposed in this paper can directly provide low-latency and highly reliable computing offloading support for Internet of Things sensor terminals, optimize the real-time processing and transmission efficiency of sensor data, and enhance the deployment and application capabilities of sensing systems in technical service scenarios.