DFT based screening approaches for discovering materials have been widely used in materials science, which, however, is not suitable for finding new magnetic materials as it consumes too much computational resources: finding the magnetic ground state of a single magnetic material is much more computationally demanding due to it has multiple antiferromagnetic configurations. Here, we developed a computational framework to de novo design novel spintronic materials by combining GAN, machine learning classifiers, and DFT. We first studied the chemical space to find the most probable combination of elements for transition metal elements-based magnetic materials. We found that ternary transition metal-based oxides and fluorides are most likely to have a magnetic ground state. Next, we developed the deep neural network(DNN) and Random forest classifier models to screen candidate magnetic materials out of the CubicGAN-generated ternary structures. Both models achieve accuracies around 90%, while DNN shows less over-training. Thus, the DNN model can work well for screening new magnetic materials. Out of 141 ternary materials generated by CubicGAN, 45 were classified by our DNN model as magnetic materials. Our DFT studies discovered that three transition metal-based oxides and one fluoride were stable magnetic compounds. Furthermore, we considered NM, FM, three AFM collinear (AFM1, AFM2, and AFM3), and two AFM noncollinear (AFM4 and AFM5) configurations to find the minimum energy states. We found that Na 6 TcO 6 , K 6 TcO 6 , and BaCuF 6 are ferromagnetic compounds while Rb 6 IrO 6 is an antiferromagnetic material with AFM1 configuration. Na 6 TcO 6 and BaCuF 6 are half metals where only spin-up bands contribute to electron conduction. Moreover, A 6 MO 6 materials hold higher thermal capacity (C v ) than that in BaCuF 6 . At 300 K temperature, C v ≈ 1100 JK -1 mol -1 for A 6 MO 6 materials, whereas C v ≈ 176 JK -1 mol -1 for BaCuF 6 . Our work demonstrates that the new generation of deep learning and machine learning based generative design framework has big potential in novel functional materials discovery.