Currently, approaches used for drug discovery, including familiar traditional methods, cannot adequately face the challenges that diabetes poses; hence, leading to the deaths of millions of people. Furthermore, ineffective drugs cannot sustain patients with diabetes well enough to prevent severe complications arising from it. GenAI now emerges to help alleviate these inconveniences, proposing innovative solutions that could revolutionize diabetes management and enhance patient outcomes. This systematic literature review aims to examine the applications and effectiveness of GenAI in drug discovery, outlining the advantages of technology’s, challenges, and future prospects. This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was performed in various databases such as Google Scholar, IEEE, Science Direct, PubMed, and Springer. Studies published between the years 2020 and 2024 were included, wherein 15 articles met the inclusion criteria. The results suggest that conventional drug development approaches, which are typically laborious, protracted, and costly, can be mitigated by GenAI techniques, including GANs, VAEs, GPT, and Diffusion models. These methodologies may manage extensive and intricate data while yielding precise outcomes. Moreover, GenAI may be utilized in the creation of pharmaceutical compounds, expediting the identification of prospective therapeutic candidates while diminishing the expenses and durations linked to drug development. Policymakers must adjust existing rules to address privacy, security, and ethical concerns associated with the use of GenAI in drug development.