The problem relies in low coverage of area and high moving distance effects overall performance of deployment in WSN. This research developed an effective coverage optimization using Improved Artificial Rabbit optimization (IARO) which is improvised by introducing Levy flight technique which helps to improvise ability during stage of exploitation. Moreover, the convergence speed and the searching accuracy is enhanced using IARO. The IARO considers energy efficiency as the major factor and enhance the coverage ability of the WSN in vast and dense environmental condition. The efficiency of the proposed approach is evaluated by considering optimized coverage rate at initial and optimized stage, moving distance of the nodes. The experimental outcome shows that the proposed approach achieved optimized coverage area of the proposed approach is 97.23% which is comparably higher than the existing techniques such as Improved Delaunay Triangulation Sparrow Search Algorithm (IM-DTSSA), Yin-Yang Pigeon Inspired Optimization (Ying-Yang PIO) and Enhanced Sparrow Search Algorithm (ESSA) with 95.63%, 88,89% and 90.59% respectively.