  In this paper, we develop a multivariate regression model and a neural
network model to predict the Reynolds number (Re) and Nusselt number in
turbulent thermal convection. We compare their predictions with those of
earlier models of convection: Grossmann-Lohse~[Phys. Rev. Lett. \textbf{86},
3316 (2001)], revised Grossmann-Lohse~[Phys. Fluids \textbf{33}, 015113
(2021)], and Pandey-Verma [Phys. Rev. E \textbf{94}, 053106 (2016)] models. We
observe that although the predictions of all the models are quite close to each
other, the machine learning models developed in this work provide the best
match with the experimental and numerical results.
