Artificial intelligence (AI) and machine learning (ML) applications are accelerated by high-performance VLSI architectures, which allow for real-time inference, analysis, and decision-making across a wide range of disciplines. The design, development, and implementation of VLSI architectures for AI and ML applications are examined in this paper, with an emphasis on scalability, efficiency, and practicality. The study's primary goals are to examine architectural paradigms, optimization strategies, energy-efficient design concepts, performance evaluation approaches, and practical uses of high-performance VLSI architectures for AI and ML. A thorough analysis of the body of research, case studies, and policy implications about VLSI design for AI and ML applications are all part of the methodology. Principal discoveries emphasize the variety of architectural paradigms, optimization strategies, and practical uses of high-performance VLSI architectures, along with their implementation difficulties and policy ramifications. The significance of ethical deliberations, adherence to regulations, and international cooperation in guaranteeing the conscientious and fair application of artificial intelligence and machine learning is highlighted by policy ramifications. By offering insights into the design, optimization, deployment, and policy implications of high-performance VLSI architectures for AI and ML applications, this study advances our collective understanding of these technologies and the field of AI-driven technologies.