This paper introduces a deep learning-based approach to the sonification of daily weather forecasts by mapping meteorological data to musical features via a latent emotion space grounded in psychological theory. Unlike traditional rule-based mappings, our model consisting of Variational Autoencoder and Feed-forward Neural Network learns associations between weather and music using numerical weather prediction data and emotion-annotated datasets. The system generates music that reflects weather conditions through features such as tempo, note duration, pitch range, and mode. Listening evaluations revealed that participants could distinguish broad weather categories (e.g., favourable vs. unfavourable), though finer distinctions proved challenging. This work highlights music’s potential as an intuitive and inclusive medium for communicating weather information, particularly for blind, visually impaired, and neurodivergent users, and supports engagement in STEAM (Science, Technology, Engineering, Arts, and Mathematics) education.