• Chemical space: Most probable combinations of elements for transition metal elements-based spintronics materials; Feature Importance of nonmagnetic-magnetic classifiers. • Structure: Material generation using generative adversarial networks (GAN) • Magnetic Ground State: Nonmagnetic-magnetic classifier development; the magnetic ground state verification using DFT. • Stability and Properties: The thermodynamic, mechanical, and dynamical stability and the property investigation using DFT First, we filter out the potential magnetic materials using information-guided approaches to lower the time consumption of the DFT calculations. Our chemical space analysis shows that the ternary transition metal element-based oxides and fluorides exhibit a higher probability of having a magnetic ground state. Furthermore, we developed a highly accurate deep neural network (DNN) and random forest classifier (RFC) to scan magnetic materials from the ternary cubic structures generated by the CubicGAN model. Feature Importance of the RFC model shows that a number of unfilled orbitals, availability of unfilled D and F orbitals, and the ground state magnetic moments of the elements play a significant role in classifying the nonmagnetic or magnetic compounds. From the predicted magnetic compounds, the DFT calculations encountered three stable transition metal elements-based oxides (Na Generating crystal structures using generative models is a much harder task than generating images or text. Several unique challenges still remain that prevent generative models to generate materials under full spectrum of the periodic table : 1) The extreme variability, such as various number of elements/atoms in the crystal structure; 2) The generation of precise fractional coordinates and lattice parameters; 3) The extreme biased distribution of materials in 230 space groups; 4) The generation of materials with high symmetry.