  Synchrotron observation serves as a fundamental tool for studying magnetic
fields in various astrophysical settings, yet its ability to unveil
three-dimensional (3D) magnetic fields-including plane-of-the-sky orientation,
inclination angle relative to the line of sight, and magnetization-remains
largely underexplored. Inspired by the latest insights into anisotropic
magnetohydrodynamic (MHD) turbulence, we found that synchrotron emission's
intensity structures inherently reflect this anisotropy, carrying detailed
information about 3D magnetic fields. Capitalizing on this foundation, we
integrate a machine learning approach-Convolutional Neural Network (CNN)-to
extract this latent information, thereby facilitating the exploration of 3D
magnetic fields. The model is trained on synthetic synchrotron emission maps,
derived from 3D MHD turbulence simulations encompassing a range of
sub-Alfv\'enic to super-Alfv\'enic conditions. We show that the CNN model is
physically interpretable and the CNN is capable of reconstructing 3D magnetic
field topology and assessing magnetization. In addition, we test our
methodology against noise and resolution effects. We show that this CNN-based
approach maintains a high degree of robustness in tracing 3D magnetic fields,
even when the low spatial frequencies of the synchrotron image are absent. This
renders the method particularly suitable for application to interferometric
data lacking single-dish measurements.
