Bridging modalities: a Survey and Taxonomy of Automated Multimodal Machine Learning (MMAutoML)

Abstract Automated machine learning (AutoML) reduces the cost of developing high-performing models by automating pipeline design, model selection, and hyperparameter optimization. As data sets increasingly combine heterogeneous sources (e.g., text, images, audio, and structured/tabular records), AutoML is extending to multimodal settings, where performance depends on representation learning and cross-modal fusion as well as model search. This survey reviews automated multimodal machine learning (MMAutoML), distinguishing core systems that automate both representation and fusion decisions from near-core systems, partial multimodal AutoML tools, and adjacent multimodal ML infrastructure. We organize multimodal learning around early, late, and hybrid fusion paradigms and discuss trade-offs in robustness, interpretability, and deployment. We then curate and compare representative open-source and commercial MMAutoML frameworks, summarize supported modalities, and characterize the ecosystem via time-evolution and similarity-based groupings. Finally, we overview applications in healthcare, autonomous systems, finance, and e-commerce, and highlight open challenges in modality alignment, missing or degraded inputs, efficiency and scalability, reproducibility, and governance (privacy, bias, and monitoring). We conclude with practical guidance and research directions toward reliable, end-to-end MMAutoML.