After the high-luminosity upgrade of the LHC, the muon chambers of CMS Barrel must cope with an increase in the number of interactions per bunch crossing. Therefore, new algorithmic techniques for data acquisition and processing will be necessary in preparation for such a high pile-up environment. Using Machine Learning as a technique to tackle this problem, this paper focuses in the production of models - with data obtained through Monte Carlo simulations - capable of predicting the transverse momentum of muons crossing the CMS Barrel muon chambers, comparing them with the transverse momentum ($p_T$) assigned by the current CMS Level-1 trigger system.