Applications for the internet of things (IoT) have grown significantly in popularity in recent years, and this has caused a huge increase in the use of cloud services (CSs). In addition, cloud computing (CC) efficiently processes and stores generated application data, which is evident in the lengthened response times of sensitive applications. Moreover, CC bandwidth limitations and power consumption are still unresolved issues. In order to balance CC, fog computing (FC) has been developed. FC broadens its offering of CSs to target end users and edge devices. Due to its low processing capability, FC only handles light activities; jobs that require more time will be done via CC. This study presents an alternative task scheduling in an IoT environment based on improving the performance of the golden jackal optimization (GJO) using the artificial hummingbird algorithm (AHA). To test the effectiveness of the developed task scheduling technique named golden jackal artificial hummingbird (GJAH), we conducted a large number of experiments on two separate datasets with varying data sizing. The GJAH algorithm provides better performance than those competitive task scheduling methods. In particular, GJAH can schedule and carry out activities more effectively than other algorithms to reduce the makespan time and energy consumption in a cloud-fog computing environment.