This paper presents a study on minimising the estimation error of forecasting the electricity consumption in Malaysia. A robust and accurate forecasts of electricity consumption are deemed crucial for the supplier to arrive on fair estimations of electricity supply optimally. Thus, identifying the best model to forecast the electricity consumption accurately may hinder energy wastage. This research aims to examine which model gives the least error in estimating the future electricity consumptions in Malaysia. Two models were tested namely Artificial Neural Network (ANN) and Regression Methods. In analysing these models, this research applies the Microsoft Excel and SAS Enterprise Miner (SAS) software. The data were extracted from the Department of Statistics Malaysia (DOSM), CEIC Data Company and The Statistics Portal. Results indicate that ANN produces least error as compared to the Regression Method as the former fits the data well whilst the latter overfits the data. The ANN model uses NNTool from MATLAB is used for forecasting future electricity consumption. The forecasted values (2020-2022) proved to provide more interpretable forecasts. This study may benefit the electricity supplier, consumers and also the Government of Malaysia, in particular the Ministry of Energy and Natural Resources. It may provide insights on estimating the optimum amount of energy to be generated. This will definitely increase the savings and reduce wastage from every angle. Ultimately, the environment is saved too.