Dexterous grasping, the ability of robotic systems to manipulate objects with human-like precision and adaptability, remains a significant challenge in robotics. We plan to develop an innovative approach for enabling dexterous grasping using deep generative models. The core idea is to leverage these models to learn the complex distribution of feasible grasps from extensive and diverse training datasets, encompassing various object geometries and physical properties. By generating a broad spectrum of potential grasp configurations, the proposed method aims to predict and execute the most effective grasp for any given object in real-time. The research will involve developing the generative model, training it with a comprehensive dataset, and testing its performance in both simulated and real-world environments. We hypothesize that our approach will enhance grasp success rates, improve robustness to novel objects, and demonstrate superior generalization capabilities compared to existing techniques. The anticipated outcome of this project is a scalable and efficient solution for dexterous robotic grasping, paving the way for more advanced and versatile robotic manipulation in dynamic and unstructured environments.