This paper explores the integration of deep learning techniques in Electronic Design Automation (EDA) tools, focusing on chip power prediction and optimization. It investigates 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 examines the transition from traditional heuristic-based methods to data-driven approaches, highlighting 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 research suggests that AI/ML technologies have the potential to revolutionize EDA workflows, enabling more efficient chip designs and accelerating innovation in the semiconductor industry. The paper discusses future directions, including the integration of quantum computing and neuromorphic architectures in EDA tools. It emphasizes 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. Key technologies discussed include attention mechanisms, machine learning, and deep learning, with a focus on their applications in EDA. Attention mechanisms enable models to prioritize critical features in chip designs, while machine learning and deep learning techniques are used for prediction, optimization, and generation. Generative adversarial networks (GANs) are highlighted for their potential in creating novel chip designs. The paper also addresses the limitations of traditional computing and storage systems in handling modern chip design complexities. It discusses the integration advantages of ML technologies, including parallel processing, adaptive learning, and high-dimensional data handling. Model interpretation and visualization techniques, such as SHAP and Grad-CAM, are presented as essential for ensuring transparency and trust in AI-driven EDA tools. Case studies are provided, showcasing applications in functional verification, QoR prediction, and OPC layout generation. The paper concludes by discussing the future potential of AI/ML in EDA tools, including the integration of quantum computing and neuromorphic architectures, and the need for more empirical research to support the efficacy of AI/ML technologies in real-world EDA scenarios.This paper explores the integration of deep learning techniques in Electronic Design Automation (EDA) tools, focusing on chip power prediction and optimization. It investigates 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 examines the transition from traditional heuristic-based methods to data-driven approaches, highlighting 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 research suggests that AI/ML technologies have the potential to revolutionize EDA workflows, enabling more efficient chip designs and accelerating innovation in the semiconductor industry. The paper discusses future directions, including the integration of quantum computing and neuromorphic architectures in EDA tools. It emphasizes 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. Key technologies discussed include attention mechanisms, machine learning, and deep learning, with a focus on their applications in EDA. Attention mechanisms enable models to prioritize critical features in chip designs, while machine learning and deep learning techniques are used for prediction, optimization, and generation. Generative adversarial networks (GANs) are highlighted for their potential in creating novel chip designs. The paper also addresses the limitations of traditional computing and storage systems in handling modern chip design complexities. It discusses the integration advantages of ML technologies, including parallel processing, adaptive learning, and high-dimensional data handling. Model interpretation and visualization techniques, such as SHAP and Grad-CAM, are presented as essential for ensuring transparency and trust in AI-driven EDA tools. Case studies are provided, showcasing applications in functional verification, QoR prediction, and OPC layout generation. The paper concludes by discussing the future potential of AI/ML in EDA tools, including the integration of quantum computing and neuromorphic architectures, and the need for more empirical research to support the efficacy of AI/ML technologies in real-world EDA scenarios.