Semiconductor device technology has greatly developed in complexity since discovering the bipolar transistor. In this work, we developed a computational pipeline to discover stable semiconductors by combining generative adversarial networks (GAN), classifiers, and high-throughput first-principles calculations. We used CubicGAN, a GAN-based algorithm for generating cubic materials and developed a classifier to screen the semiconductors and studied their stability using first principles. We found 12 stable AAMH₆ semiconductors in the F-43m space group including BaNaRhH₆, BaSrZnH₆, BaCsAlH₆, SrTlIrH₆, KNaNiH₆, NaYRuH₆, CsKSiH₆, CaScMnH₆, YZnMnH₆, NaZrMnH₆, AgZrMnH₆, and ScZnMnH₆. Previous research reported that five AAIrH6 semiconductors with the same space group were synthesized. Our research shows that AAMnH₆ and NaYRuH₆ semiconductors have considerably different properties compared to the rest of the AAMH₆ semiconductors. Based on the accurate hybrid functional calculations, AAMH₆ semiconductors are found to be wide-bandgap semiconductors. Moreover, BaSrZnH₆ and KNaNiH₆ are direct-bandgap semiconductors, whereas others exhibit indirect bandgaps.