Accurate and trustworthy energy consumption prediction is necessary due to the increasing demand for effective energy management in today’s dynamic environment like smart home energy management systems, utility company tools, industrial systems, etc. This study employs deep learning models, such as Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), and Long Short-Term Memory (LSTM), for energy-usage forecasting through time series analysis. The goal of the study is to evaluate and contrast these model’s predictive ability in terms of forecasting patterns of household energy consumption. By analyzing the predictive accuracy and efficiency of each model, this research informs decision-making in energy management practices. The results of our experimental analysis for the BiLSTM were as follows: Mean Absolute Error (MAE) of 0.409, Root Mean Square Error (RMSE) of 0.604, and Mean Absolute Percentage Error (MAPE) of 48.268. The next greatest RMSEs and MAEs were recorded by the LSTM and GRU, with 0.606, 0.409, and 49.518, respectively. The results demonstrate that when compared to LSTM and GRU, BiLSTM has a promising role in improving energy consumption prediction and enabling more informed and efficient energy usage in households.