Multimodal Learning is a machine learning approach that jointly models information from multiple data types such as text, images, audio, video, and sensor signals. It learns shared or aligned representations to support tasks like classification, retrieval, captioning, and question answering. Multimodal learning is used when combining complementary signals improves robustness, context understanding, and generalization across different input sources.