The eEF2K, a member of the α-kinase family, plays a crucial role in cellular differentiation and the stability of the nervous system. The development of eEF2K inhibitors has proven to be significantly important in the treatment of diseases such as cancer and Alzheimer’s. With the advancement of big data in pharmaceuticals and the evolution of molecular generation technologies, leveraging artificial intelligence to expedite research on eEF2K inhibitors shows great potential. Based on the recently published structure of eEF2K and known inhibitor molecular structures, a generative model was used to create 1094 candidate inhibitor molecules. Analysis indicates that the model-generated molecules can comprehend the principles of molecular docking. Moreover, some of these molecules can modify the original molecular frameworks. A molecular screening strategy was devised, leading to the identification of five promising eEF2K inhibitor lead compounds. These five compound molecules demonstrated excellent thermodynamic performance when docked with eEF2K, with Vina scores of −12.12, −16.67, −15.07, −15.99, and −10.55 kcal/mol, respectively, showing a 24.27% improvement over known active inhibitor molecules. Additionally, they exhibited favorable drug-likeness. This study used deep generative models to develop eEF2K inhibitors, enabling the treatment of cancer and neurological disorders.