The understanding and diagnosis of emergent topological order in an electronic band structure at low temperatures has progressed immensely over the last fifteen years. Once thought to be rare, a recent work found that over 88\% of materials in the inorganic crystal structure database (ICSD) support at least a single topological band. While these results cast a façade that the promise of topological quantum materials has been fulfilled, the reality is far more complicated. Many entries in the materials database are listed as topological insulators but support complicated band structures with multiple electron/hole pockets. These aspects can make them unsuitable for experiments or device applications. In fact, only a handful of high-quality topological materials have been experimentally realized. The scarcity of high-quality topological materials has created a significant practical bottleneck, preventing the realization of groundbreaking advancements in quantum computing, quantum sensors}, spintronics, topological photonics thermoelectric devices, magnetic memory, and many other technological applications. My research will focus on the design of two-dimensional topological materials to overcome this challenge. To accomplish this task, I will develop an inverse design protocol relying on generative artificial intelligence (GAI). The tools and knowledge to construct GAI has advanced significantly in recent years. In parallel to the industrial development of large language models (LLMs) such as ChatGPT and Google Bard, there has been a concerted effort in the field of materials science to utilize GAI in the design of materials for catalysis and carbon capture to confront the energy and climate crisis, among many other open and immediate challenges.