Low-temperature plasma plays various roles in industrial material processing as well as provides a number of scientific targets. Such rich features in variety are based on its complexities, arising from its diverse parameters. When we consider causalities in these complexities, direct application of machine-learning methods is not always possible. To overcome this difficulty, progresses in plasma diagnostics and data acquisition systems are inevitable, and handling of a large number of data elements is being one of the key issues for this purpose. In this topical review, we summarize previous and current achievements of visualization, acquisition, and analysis methods for complex plasma datasets which may open a scientific and technological category mixed with rapid machine-learning progresses and their relevant outcomes. Many reports published so far have already convinced us of various expanding aspects for low-temperature plasma, leading to potentials for scientific progresses as well as intellectual design in industrial plasma processes.