The edge computing is a developing architecture that takes storage and computation resources to edge network and generates optimal services and applications near to the end-users. The existing methods are suffered from high energy consumption and delay which leads to reduce the performance. Therefore, this research proposed a Sine Cosine Learning Factor with artificial jellyfish search optimization (SCAJSO)-based resource allocation for edge computing in Internet of Things (IoT). With the proposed SC learning factors, the consideration and learning from both the global optimum agent solutions and the random solutions within the search space offer enhanced learning. This developed learning mechanism increases convergence speed further to facilitate faster resources allocation. SCAJSO also helps to avoid premature convergence and maintain relatively equal contribution of the search space. The SCAJSO achieves throughput of 0.937 Mbps, energy of 42.78 mJ and delay of 5.73 ms for 100 number of tasks which is better than other state-of-art methods.