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To overcome this problem, a novel deep learning and machine learning approach based on long short-term memory (LSTM) and Extreme Gradient Boosting (XGBoost) was applied to predict storm characteristics and occurrence in Western France. A combination of data from buoys and a storm database between 1996 and 2020 was processed for model training and testing. The models were trained and validated with the dataset from January 1996 to December 2015 and the trained models were then used to predict storm characteristics and occurrence from January 2016 to December 2020. The LSTM model used to predict storm characteristics showed great\u00a0accuracy in forecasting\u00a0temperature and pressure, with challenges observed in capturing extreme values for\u00a0wave\u00a0height and wind speed. The trained XGBoost model, on the other hand, performed extremely well in predicting storm occurrence. 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