Crop diseases, changing climatic scenarios, and the need for effective resource management such as water and soil nutrients are some of the difficulties faced by agricultural sector. The success so far with traditional agricultural practices have become insufficient consistently to address these issues. It is crucial to find innovative methods to improve food production and security, and promote sustainable farming. By using sophisticated methods in agriculture like Generative AI (GenAI), Generative Adversarial Networks (GANs), an innovative solution to agricultural problems can be resolved. The GANs create synthetic data, optimize precision agriculture, identify diseases and pests, manage soil health, and develop methods for irrigation and climate adaption. However, the agriculture sector yet to realize the potential of GAN to maximum extent. The role and uses of GANs in agriculture are examined in this study, with an emphasis on their advantages over conventional deep learning techniques. The primary objective of this study is to present an exhaustive technological framework for the application of GANs and generative AI (GenAI) in agriculture. This work aims to equip farmers with cutting-edge tools and techniques that improve adaptability and responsiveness to the difficulties present in the agricultural sector by providing information into the real-world applications of these sophisticated models.