Multimodal learning is a deep learning approach that seeks to combine multiple types of data, such as text and images, to create richer representations for improved performance in tasks like feature extraction and classification. Unlike traditional deep learning models that typically handle unimodal data, multimodal models aim to provide a more comprehensive understanding by processing sequential relationships in text and spatial relationships in images simultaneously. This approach is especially beneficial when data sources are incomplete, as it allows for better inference by leveraging available information across modalities. Researchers have explored various methods to create multimodal embeddings, either by training models from scratch or by fusing pre-trained unimodal embeddings to enhance feature richness. Applications of multimodal learning include image captioning, visual question answering, and visual reasoning, where integrating multiple data types can provide more context and accuracy. As deep learning continues to evolve, the ability to process diverse and incomplete data sources will become increasingly vital for the development of advanced technologies.