Over the last years, Machine Learning (ML) tools have been successfully applied to a wealth of problems in high-energy physics. In this work, we discuss the extraction of the average number of Multiparton Interactions (⟨Nmpi⟩) from minimum-bias pp data at LHC energies using a ML regression based on Boosted Decision Trees. Using the available ALICE data on transverse momentum spectra as a function of multiplicity, we report that for minimum-bias pp collisions at s√= 7 TeV the average Nmpi is 3.98 ± 1.01, which complements our previous results for pp collisions at s√= 5.02 and 13 TeV. The comparisons indicated a modest center-of-mass energy dependence of ⟨Nmpi⟩. The study is further extended extracting the multiplicity dependence of Nmpi for the three center-of-mass energies. These results are qualitatively consistent with the existing ALICE measurements sensitives to MPI. Through the ML method applied to pp collisions at s√ = 13 TeV, we also show that computing the multiplicity in the forward region the extraction of Nmpi improves. Which opens the possibility to extract the number of MPI event-by-event, and in this way study the particle production as a function of MPI instead of the multiplicity estimator defined by each experiment. Our results provide additional evidence of the presence of MPI in pp collisions, and can help to the understanding of the heavy-ion-like behaviour observed in pp collisions data.