  Machine learning has had an enormous impact in many scientific disciplines.
Also in the field of low-temperature plasma modeling and simulation it has
attracted significant interest within the past years. Whereas its application
should be carefully assessed in general, many aspects of plasma modeling and
simulation have benefited substantially from recent developments within the
field of machine learning and data-driven modeling. In this survey, we approach
two main objectives: (a) We review the state-of-the-art focusing on approaches
to low-temperature plasma modeling and simulation. By dividing our survey into
plasma physics, plasma chemistry, plasma-surface interactions, and plasma
process control, we aim to extensively discuss relevant examples from
literature. (b) We provide a perspective of potential advances to plasma
science and technology. We specifically elaborate on advances possibly enabled
by adaptation from other scientific disciplines. We argue that not only the
known unknowns, but also unknown unknowns may be discovered due to the inherent
propensity of data-driven methods to spotlight hidden patterns in data.
