We present the results of a study where we use machine learning to enhance hohlraum design for opacity measurement experiments. Opacity experiments on laser facilities use hohlraums, which, when their interior walls are illuminated by the National Ignition Facility (NIF) lasers, produce a high radiation flux that heats a central sample to a temperature that is constant over a measurement time window. Given a baseline hohlraum design and a computational model, we train a deep neural network to predict the time evolution of the radiation temperature as measured by the Dante diagnostic. This enables us to rapidly explore design space and determine the effect of adjusting design parameters. We also construct an “inverse” machine learning model that predicts the design parameters given a desired time history of radiation temperature. Calculations using the machine learning model demonstrate that improved performance over the baseline hohlraum could reduce sensitivities and uncertainties in experimental opacity measurements. •We demonstrate that machine learning models can be used to augment simulation to design opacity experiments.•Forward machine learning models allow design space to be explored.•We develop a machine learning model to learn the inverse mapping from diagnostics to experiment design.•Simulations indicate that measurement uncertainties can be improved using our models.