This review provides a comprehensive overview of energy forecasting (EF) techniques and technologies, focusing on their applications in the energy sector. The paper discusses various forecasting methods, including statistical, machine learning (ML), and deep learning (DL) approaches, as well as ensemble methods. It highlights the importance of accurate EF for efficient energy management, grid stability, and the integration of renewable energy sources. The review covers different time horizons for load and generation forecasting, ranging from very short-term (VSTF) to long-term (LTF). It also examines the input parameters used in EF, such as weather data, historical load data, and socio-technical factors. The paper evaluates various forecasting models, including linear regression, tree-based models, and deep learning models like LSTM and CNN, and discusses their performance metrics. It also explores the challenges and future directions in EF, emphasizing the need for improved accuracy and adaptability to changing energy systems. The review concludes that EF plays a crucial role in optimizing energy resources, enhancing grid reliability, and supporting sustainable energy management. The study provides a detailed analysis of the current state of EF, identifying key trends, challenges, and opportunities for future research.This review provides a comprehensive overview of energy forecasting (EF) techniques and technologies, focusing on their applications in the energy sector. The paper discusses various forecasting methods, including statistical, machine learning (ML), and deep learning (DL) approaches, as well as ensemble methods. It highlights the importance of accurate EF for efficient energy management, grid stability, and the integration of renewable energy sources. The review covers different time horizons for load and generation forecasting, ranging from very short-term (VSTF) to long-term (LTF). It also examines the input parameters used in EF, such as weather data, historical load data, and socio-technical factors. The paper evaluates various forecasting models, including linear regression, tree-based models, and deep learning models like LSTM and CNN, and discusses their performance metrics. It also explores the challenges and future directions in EF, emphasizing the need for improved accuracy and adaptability to changing energy systems. The review concludes that EF plays a crucial role in optimizing energy resources, enhancing grid reliability, and supporting sustainable energy management. The study provides a detailed analysis of the current state of EF, identifying key trends, challenges, and opportunities for future research.