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Fast-developing single-cell multimodal omics (scMulti-omics) technologies enable the measurement of multiple modalities, such as DNA methylation, chromatin accessibility, RNA expression, protein abundance, gene perturbation, and spatial information, from the same cell. scMulti-omics can comprehensively explore and identify cell characteristics, while also presenting challenges to the development of computational methods and tools for integrative analyses. Here, we review these integrative methods and summarize the existing tools for studying a variety of scMulti-omics data. The various functionalities and practical challenges in using the available tools in the public domain are explored through several case studies. Finally, we identify remaining challenges and future trends in scMulti-omics modeling and analyses. Applying integrative methods to scMulti-omics data opens a new window into the understanding of heterogeneous mechanism landscapes and cell–cell interactions. Integration of cross-experiment data poses a special challenge. A comprehensive understanding of the underlying methods is necessary to determine which pipeline is appropriate for a given scMulti-omics data set. We designed and implemented two case studies to demonstrate the application of available scMulti-omics tools, where new insights and practical challenges are generated. Among the numerous remaining challenges in scMulti-omics, establishing a robust benchmarking pipeline is paramount. Trends observed in traditional multi-omics, including machine learning, artificial intelligence, and evolving technologies, are paralleled in scMulti-omics methods. [ABSTRACT FROM AUTHOR]