In response to advancements in virtualization technologies, such as containers and virtual machines, the adoption of data-driven methodologies, and the emergence of software-defined networking technologies, such as data plane programming, a new emphasis on integrating computation and communication in distributed systems has emerged. Consequently, this has led to edge-cloud continuum computing to emerged because of the rise of edge computing as a supplement to and sometimes even a replacement for traditional cloud services. Because networking and computers have always evolved separately, combining the two is a challenge. Task scheduling is one of the challenges encountered in an edge-cloud continuum computing environment. In this case, determining where applications (Tasks) should be executed is crucial for meeting the quality-of-service needs of the applications. Therefore, an edge-cloud system needs a powerful task scheduler to determine the optimal locality (edge, cloud, or both) for task execution. An efficient scheduling strategy must be used to ensure that the workloads allocated to the virtual machines in the edge-cloud continuum datacenter are distributed to fulfil the Quality of Service (QoS) (e.g., time, cost, latency, minimum bandwidth) requirements of all users. QoS is an important problem for both resource providers and mobile consumers. It’s obvious that there is no research that presented an ideal task scheduling algorithm on an edge-cloud continuum that efficiently solve the QoS requirements for both customers and providers. In this research, we employ the potentials of the metaheuristic flower pollination algorithm (PFA) in solving task scheduling problems in an edge-cloud continuum environment. Simulation results show that FPA performs better over the famous PSO with significant improvements in terms of makes pan time especially when the number of tasks grows higher.