The NuMI target facility produces an intense muon neutrino beam for long baseline neutrino experiments. A muon monitor which measures muon beam profile, is a key beam element to maintain the quality of muon neutrino beam. Three arrays of muon monitors located in the downstream of the hadron absorber provide the measurements of the primary beam quality. We studied the response of muon monitors with the proton beam profile changes and focusing horn current variations. The responses of muon monitors have been used to implement Machine Learning (ML) algorithms to monitor the beam quality. Applications of ML techniques have shown the capabilities of identifying incidents, predicting the beam parameters and horn current with a significant accuracy. I will present the current status, recent results and future plans from our muon monitor data studies.