The increasing need for incorporating intelligence (AI) into edge computing has driven progress in refining techniques for optimizing microprocessors. The survey examines the recent hardware and software approaches designed to improve the effectiveness, power usage, and computational capability of microprocessors for edge AI operations. This study emphasizes the enhancement of microprocessor architectures specifically tailored for edge AI applications. The primary obstacles in optimizing microprocessors for AI functions revolve around concerns like management and connectivity challenges, preserving privacy and security, and addressing the limitations of hardware components. This survey outlines trends and areas for improvement in the design of AI-driven microprocessors to guide advancements. In the realm of technology development, there is a focus on optimizing microprocessors for edge devices to enhance efficiency and reduce power consumption in deep learning applications.