This study examines how generative AI models can be utilized for the process optimization of the semiconductor wafer design and predict the yield leading to the semiconductor wafer design. The study pays attention to the use of deep learning algorithms, such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and reinforcement learning, to advance semiconductor manufacturing efficiency, precision, and yield. A number of AI methods were used to optimize the wafer design, identify the malfunctions, and calculate the yield according to the process parameters. As the result showed, the AI-driven models were better at the defect detection and the yield prediction than the traditional approaches, as the difference in the accuracy of the established models compared to the conventional models constituted 15% improvement. To be specific, the CNN based defect detection model achieved an accuracy reading of 92% whereas the GAN based anomaly detection model recorded a precision rate of 89%. Also, the reinforcement learning model resulted in the reduction of the process iterations needed in order to optimize the wafer design by 20%. These results demonstrate the future capabilities of AI in optimizing the process efficiency and reliability in semiconductor manufacturing, facilitating real-time defect detection, and creating more accurate measures of yield. This research gives promise to the role of AI in the transformation of the manufacturing of semiconductors through the optimization of the process design as well as the prediction of yields that later comes to reduce the costs and bring the production quality up to par.