Multimodal machine learning aims to build models that can process and relate information from multiple modalities, such as vision, audio, and language. This survey presents a taxonomy of the five core technical challenges in multimodal machine learning: representation, translation, alignment, fusion, and co-learning. The paper discusses recent advances in these areas and highlights the importance of addressing these challenges for progress in the field. Multimodal machine learning has a wide range of applications, including audio-visual speech recognition, image captioning, and multimedia content indexing. The paper also discusses the challenges of representing multimodal data, translating between modalities, aligning different modalities, fusing information from multiple modalities, and co-learning across modalities. The survey concludes with a discussion of the future directions for research in multimodal machine learning.Multimodal machine learning aims to build models that can process and relate information from multiple modalities, such as vision, audio, and language. This survey presents a taxonomy of the five core technical challenges in multimodal machine learning: representation, translation, alignment, fusion, and co-learning. The paper discusses recent advances in these areas and highlights the importance of addressing these challenges for progress in the field. Multimodal machine learning has a wide range of applications, including audio-visual speech recognition, image captioning, and multimedia content indexing. The paper also discusses the challenges of representing multimodal data, translating between modalities, aligning different modalities, fusing information from multiple modalities, and co-learning across modalities. The survey concludes with a discussion of the future directions for research in multimodal machine learning.