In the smart grid, load forecasting algorithms used for estimating the electricity demand based on historical data. It helps in generating accurate electricity and overcoming the two challenges such as a shortage of electricity and excess generation cost. In the literature, various traditional load forecasting algorithms proposed to predict the electricity demand but never the accurate results. Therefore, advanced algorithms come into the picture such as artificial intelligence algorithms. In this paper, we have hybrid the Artificial Neural Network (ANN) and firefly algorithm for load prediction. Initially, the ANN algorithm is trained based on the historical data then applied to it. After that, the firefly algorithm is used for searching for the optimal learning rate for ANN. The experimental results are performed in the MATLAB 2015a. We have measured various performance analysis parameters and compared with the existing results. From the study, we found that the proposed algorithm gives better accuracy as compared to the existing algorithms