Multimodal Forecasting is the prediction of future states or events by integrating data from multiple input modalities, such as time series, images, text, and sensor streams, within a unified model. By combining complementary sources of information, multimodal forecasting systems achieve greater predictive accuracy than single-modality approaches. Applications include traffic and demand forecasting, weather prediction, financial modeling, and autonomous driving, where contextual signals across data types improve both short- and long-horizon predictions.