aspg-28-3234-1793997662 20261106204102 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2024 8 1 Mona Ahmed Yassen Faculty of Artificial Intelligence, Horus University, New Damietta, Egypt Mohamed Gamal Abdel-Fattah Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt Islam Ismail Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt EL-Sayed M. El Kenawy School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain Hossam El-Din Moustafa Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt Accurate forecasting of renewable energy source (RES) generation is increasingly important for effective grid operation and energy management as RES are integrated into electrical grids. Machine learning (ML) and deep learning (DL) algorithms can learn complex relationships from data and provide forecasts, addressing limitations of traditional approaches. This review examines the state of the art and future directions in ML- and DL-based renewable energy forecasting. It surveys the approaches and models used to predict renewable generation and highlights challenges including output uncertainty and variability, data accessibility, and model interpretability. Developing accurate and dependable forecasting models is essential to support the transition to sustainable energy and the integration of RES into electrical grids. 2024 01 05 3234 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.080101 https://americaspg.com/journal/28/article/3234 https://americaspg.com/storage/articles/manuscripts/01M5B0NCR4KG4B7PMAF4RPRT3N.pdf