In the field of machine learning, multimodal learning refers to a deep learning approach that leverages several data modalities, including text, audio, and images. The objective of multimodal learning is to learn a set of modality-specific transformations that map samples from different modalities into a shared space. This conference paper provides a discussion on the methods of multimodal transfer learning, focusing particularly on how to leverage multiple data resources. This paper also reviews the application of transfer learning in various fields such as computer vision, natural language processing, neural networks, and applications in sectors such as the autonomous vehicle industry, E-commerce, the gaming industry, and the healthcare sector. The experimental results highlight the significance of choosing suitable transfer learning models for various real-world applications.