Analyzed and forecasted U.S. energy production and consumption trends using 50 years of historical data, emphasizing primary energy imports, exports, and consumption in industrial and residential sectors. Collected, cleansed, and analyzed data to uncover seasonal patterns and key influencing factors. Built and evaluated predictive models, including ARIMA, SARIMA, RNN, LSTM, GRU, and XGBoost, to forecast energy trends over a five-year horizon. Demonstrated superior performance with machine learning models, particularly GRU, which captured complex seasonal trends with the highest accuracy and lowest error. Delivered actionable insights through time-series forecasting, deep learning model development, and comparative analysis to support informed decision-making in the energy sector.