Generative AI has been considered to fit Science and Technology especially in Graph Theory and Software Testing and as such the subject has attracted some attention. Based on the literature, this paper reviews previous works concerning the effect of Generative AI in these fields owing to the call to build more knowledge, address challenges, and improve practices. Graph theory which is a branch of discrete mathematics is vital in computer science and operation research. For this field, generative AI would help in the discovery of graphs, algorithms as well as solutions. With this proposal, this study examines how the existing GANs and VAEs are applied to graph generation, node prediction, and anomaly detection before pointing out the existing research gaps and future research avenues. Likewise, Software Testing essential in system development is evolving with Generative AI. The conventional ways are thus more cumbersome and very often inaccurate. In terms of testing, generative AI can come up with test cases, predict failures, and generate smart data therefore changing the test techniques. This paper discusses and analyze models of AI used for test automation and identification of defects for test automation. Consequently, comparing the literature, the paper elucidates that Generative AI transforms Graph Theory and Software Testing while promoting interdisciplinary cooperation.