Muon collisions are considered a promising means for exploring the energy frontier, leading to a detailed study of the possible feasibility challenges. Beam intensities of the order of 1012 muons per bunch are needed to achieve the necessary luminosity, generating a high flux of secondary and tertiary particles from muons decay that reach both the machine elements and the detector region. To limit the impact of this background on the physics performance, tungsten shieldings have been studied. A machine learning-based approach to the geometry optimization of these shieldings will be discussed.