This chapter comprehensively explores the application of big data and machine learning in energy forecasting. Traditional statistical models like ARIMA struggle with the complexity and nonlinearity of modern energy systems, while machine learning models, especially deep learning algorithms such as LSTM, GRU excel at capturing nonlinear relationships and have significantly enhanced forecasting accuracy. Big data analytics integrates diverse data sources, including smart grids, weather systems, and consumption patterns, offering more precise and timely forecasts that optimize energy management. However, challenges like data quality, computational resource limitations, and model interpretability persist.