Streets serve as the foundational elements of urban environments. Their visual characteristics directly reflect urban spatial quality and influence residents' well-being. Street view imagery captures street scenes from a human perspective, providing a unique viewpoint for micro-level urban environment analysis and serving as an indispensable resource for urban planning. This study systematically reviews the development of street view image applications in urban planning, especially focusing on their integration with advancing artificial intelligence (AI) techniques. From early manual audits to feature extraction powered by statistical machine learning, and later to automated analysis driven by deep learning, the efficiency and accuracy of street view images utilization have markedly improved. In recent years, the incorporation of self-supervised learning and large language models has markedly enhanced the application potential of street view images, enabling more complex urban analysis compared to earlier approaches. By reviewing the application of street view data across different technological stages, this study illustrates how artificial intelligence has reshaped its analytical paradigms and explores its promising potential for future urban planning.