This paper explores the integration of deep learning techniques in Electronic Design Automation (EDA) tools, focusing on chip power prediction and optimization. The authors investigate the application of advanced AI technologies, including attention mechanisms, machine learning, and generative adversarial networks (GANs), to address complex challenges in modern chip design. The study highlights the transition from traditional heuristic-based methods to data-driven approaches, emphasizing the potential for significant improvements in design efficiency and performance. The paper presents case studies demonstrating the effectiveness of AI-driven EDA tools in functional verification, Quality of Results (QoR) prediction, and Optical Proximity Correction (OPC) layout generation. It also addresses critical challenges such as model interpretability and the need for extensive empirical validation. The findings suggest that AI/ML technologies have the potential to revolutionize EDA workflows, enabling more efficient chip designs and accelerating innovation in the semiconductor industry. The authors discuss future directions, including the integration of quantum computing and neuromorphic architectures in EDA tools. They emphasize the importance of collaborative research between AI experts and chip designers to fully realize the potential of these technologies and address emerging challenges in advanced node designs.This paper explores the integration of deep learning techniques in Electronic Design Automation (EDA) tools, focusing on chip power prediction and optimization. The authors investigate the application of advanced AI technologies, including attention mechanisms, machine learning, and generative adversarial networks (GANs), to address complex challenges in modern chip design. The study highlights the transition from traditional heuristic-based methods to data-driven approaches, emphasizing the potential for significant improvements in design efficiency and performance. The paper presents case studies demonstrating the effectiveness of AI-driven EDA tools in functional verification, Quality of Results (QoR) prediction, and Optical Proximity Correction (OPC) layout generation. It also addresses critical challenges such as model interpretability and the need for extensive empirical validation. The findings suggest that AI/ML technologies have the potential to revolutionize EDA workflows, enabling more efficient chip designs and accelerating innovation in the semiconductor industry. The authors discuss future directions, including the integration of quantum computing and neuromorphic architectures in EDA tools. They emphasize the importance of collaborative research between AI experts and chip designers to fully realize the potential of these technologies and address emerging challenges in advanced node designs.