This paper reports the use of machine learning to enhance the diagnosis of a dusty plasma. Dust in a plasma has a large impact on the properties of the plasma. According to a probe diagnostic experiment on a dust-free plasma combined with machine learning, an experiment on a dusty plasma is designed and carried out. Using a specific experimental device, dusty plasma with a stable and controllable dust particle density is generated. A Langmuir probe is used to measure the electron density and electron temperature under different pressures, discharge currents, and dust particle densities. The diagnostic result is processed through a machine learning algorithm, and the error of the predicted results under different pressures and discharge currents is analyzed, from which the law of the machine learning results changing with the pressure and discharge current is obtained. Finally, the results are compared with theoretical simulations to further analyze the properties of the electron density and temperature of the dusty plasma.