Accurately predicting electrical load is crucial for effective energy management in green buildings (GB). However, the task of forecasting electricity consumption is inherently challenging due to the dynamic nature of indoor environmental changes. This study addresses this issue by employing a Long Short-Term Memory (LSTM) deep learning model to predict energy load in green buildings. By utilizing a month’s historical data encompassing temperature, humidity, and energy consumption, the LSTM model is trained to forecast energy load. The results demonstrated the effectiveness of the LSTM model in predicting energy load, with impressive performance metrics of R2 Score of 0.992 when forecasting energy load for a week. This research contributes to the field of energy management in green buildings by providing a reliable and efficient method for predicting electrical load, ultimately aiding in integrating GB into smart grids.