<p>The Industrial Internet of Things (IIoT) has brought about a profound transformation in industries by facilitating intelligent data exchange among interconnected devices. However, optimizing performance, reliability, and efficiency in IIoT systems involves overcoming several obstacles, including energy consumption, latency, and scalability, which are still open issues. To overcome these challenges, we propose an improvised IIoT-Multiaccess Edge Computing model by tackling task offloading while considering load balancing and dependency of tasks in a heterogeneous environment. The proposed approach addresses load balancing and computation offloading in an orthogonal frequency division multiplexing (OFDM) network with multiple IIoT end devices connected to a single base station with an edge server. We have used ordinal multi-objective optimization (OMOO) for load balancing to effectively identify high-quality solutions for optimized queue span and link quality. The optimal offloading decision algorithm (OODA) is employed to minimize long-term energy utilization while reducing average traffic congestion. The proposed model is evaluated by comparing it with various offloading systems based on multiple metrics, such as energy consumption and traffic delay, to understand how well the model performs relative to others. The simulation results show that the proposed model excels in energy savings, scalability, and latency reduction for IIoT-edge systems. It effectively satisfies quality of service (QoS) specifications and enhances energy conversion efficiency.</p>