<p>In recent years, IoT-enabled devices have become widespread, varying significantly in size, computational power, and communication capabilities. However, they generate vast amounts of data, creating a significant challenge for centralized cloud storage and processing. Edge computing address these issues by enabling data processing at the edge of the network. However, the ever-increasing IoT tasks can quickly overburden edge nodes, as they have limited resources. Additionally, efficiently processing time-sensitive tasks is a major concern. To address this issue, this paper proposes an efficient task management and offloading scheme for edge-cloud IoT systems (termed as OTM). The proposed scheme leverages fuzzy logic system to determine the most suitable layer—cloud or edge—for offloading IoT tasks and organizes edge nodes into uniform clusters based on proximity. Furthermore, an optimization function has been formulated for task scheduling at the edge layer, which minimizes the response time of tasks by considering factors such as WAN delay, transmission delay at edge nodes, and the energy of edge nodes. This scheme aims to balance the load among edge nodes, optimize resource utilization, improve energy efficiency, and efficiently process IoT tasks while considering various characteristics of tasks. According to the simulation results the proposed scheme reduces the percentage of failed tasks by 30%and 32% compared to FuB (fuzzy-based algorithm) and Fuzzy workload orchestration schemes, respectively. Furthermore, the proposed scheme optimizes energy consumption across the network by distributing workloads evenly among edge nodes. Additionally, performance metrics such as average processing time, service time, and network delay are also evaluated, highlighting the effectiveness of the proposed scheme.</p>