The extensive proliferation of renewable energy sources and complex loads stresses the power system operation, making it challenging to ensure the safe and effective operation of networks. In this scenario, forecasting voltage may improve the effectiveness of voltage regulation against critical events. For this reason, this paper explores the effectiveness of machine learning models to predict voltage excursion events in power systems using simple categorical labels. By treating the prediction as a categorical classification task, the workflow is characterized by a low computational and data burden. A proof-of-concept case study on a real portion of the Italian 150 kV sub-transmission network, which hosts a significant amount of wind power generation, demonstrates the general validity of the proposal. A detailed comparison of evaluation metrics offers insight into the strengths and weaknesses of several widely utilized prediction models for this application in the presence of balanced and unbalanced datasets.