This article discusses the utilization of Artificial Neural Networks for predicting wind power generation in Uruguay. The precise estimation of power output plays a vital role in designing a dependable wind power generation infrastructure. Accurate predictions enable the implementation of efficient planning, management, and distribution strategies for the produced power, thereby enhancing the performance and the efficacy of the system. The research incorporates actual wind power generation data from Uruguay spanning the period between 2018 and 2022. The forecasting is performed using a Long Short-Term Memory artificial neural network. The primary findings suggest that the proposed methodology has a good prediction accuracy, with an average root mean square error of 0.12. The computed forecasts can be effectively employed for planning and scheduling to enhance service quality.