Local measurements of the Hubble constant (${H}_{0}$ ) based on Cepheids e Type Ia
supernova differ by $\approx 5\sigma $ from the estimated value of ${H}_{0}$ from
Planck CMB observations under $\mathrm{\Lambda}$ CDM assumptions. In order to better
understand this ${H}_{0}$ tension, the comparison of different methods of analysis
will be fundamental to interpret the data sets provided by the next generation
of surveys. In this paper, we deploy machine learning algorithms to measure the
${H}_{0}$ through a regression analysis on synthetic data of the expansion rate
assuming different values of redshift and different levels of uncertainty. We
compare the performance of different algorithms as Extra-Trees, Artificial
Neural Network, Extreme Gradient Boosting, Support Vector Machines, and we find
that the Support Vector Machine exhibits the best performance in terms of
bias-variance tradeoff, showing itself a competitive cross-check to
non-supervised regression methods such as Gaussian Processes.