Solar wind models are highly dependent on global magnetic fields at the solar surface as their inner boundary condition, and the lack of global field data is a significant problem plaguing solar wind modeling. Currently, only direct observations of the near-side magnetic field exist and far-side approximations are incapable of predicting growth of existing active regions or new magnetic flux emergence. To fill this data gap, we develop a method that calibrates far-side helioseismic images, which are calculated using near-side Doppler observations, to far-side magnetic flux maps. The calibration employs multiple machine-learning methods that use EUV 304 Å data as a bridge. These algorithms determine a relation 1) between the near-side AIA 304 Å data and HMI magnetic field data, and 2) between STEREO 304 Å data and far-side helioseismic images obtained from a newly developed time-distance helioseismic far-side imaging method. The resulting magnetic flux maps have been further calibrated using maps produced by a flux transport model. The various data products from this work — far-side acoustic maps, far-side STEREO EUV-derived magnetic flux maps, and near-real-time acoustically-driven far-side magnetic flux maps, along with maps of the associated uncertainties — are being made available to enable a synchronic global magnetic flux input into coronal and solar wind models.