This study aims to develop wind energy prediction models using machine learning techniques as part of a national-level Renewable Energy Communities (REC) optimization planning platform. The study focuses on predicting wind energy production potential across municipalities by utilizing multi-source environmental variables, including regional topography, surface characteristics, and meteorological factors. Through data collection, preprocessing, and the application of machine learning algorithms, we analyze the relationship between wind energy production and input variables. The resulting model will be integrated into the platform to generate detailed wind energy production forecasts, providing scientific evidence for planners to support optimal wind resource allocation and energy management. This research provides technical support for the platform's wind energy module, facilitating sustainable development of renewable energy communities.