With the technological evolution of mobile devices, 5G and 6G communication and users' demand for new generation applications viz. face recognition, image processing, augmented reality, etc., has accelerated the new computing paradigm of Mobile Edge Computing (MEC). It operates in close proximity to users by facilitating the execution of computational-intensive tasks from devices through offloading. However, the offloading decision at the device level faces many challenges due to uncertainty in various profiling parameters in modern communication technologies. Further, with the increase in the number of profiling parameters, the fuzzy-based approaches suffer inference searching overheads. In this context, a fuzzy-based approach with an optimal inference strategy is proposed to make a suitable offloading decision. The proposed approach utilizes the Classification and Regression Tree (CART) mechanism at the inference engine with reduced time complexity of O (|V|2log2| L|)), as compared to O (| L ||V|) of state-of-the-art, conventional fuzzy-based offloading approaches, and has been proved to be more efficient. The performance of the proposed approach is evaluated and compared with contemporary offloading algorithms in a python-based fog and edge simulator, YAFS. The simulation results show a reduction in average task processing time, average task completion time, energy consumption, improved server utilization, and tolerance to latency and delay sensitivity for the offloaded tasks in terms of reduced task failure rates.