Recent advances in artificial intelligence (AI) have led to the development of sophisticated algorithms that significantly improve image analysis capabilities. This combination of AI and microscopic imaging is transforming the way we interpret and analyze imaging data, simplifying complex tasks and enabling innovative experimental methods previously thought impossible. In smart manufacturing, these improvements are especially impactful, increasing precision and efficiency in production processes. This review examines the convergence of AI with particle image analysis, an area we refer to as “particle vision analysis (PVA)." We offer a detailed overview of how this technology integrates into and impacts various fields within the physical sciences and materials sectors, where it plays a crucial role in both innovation and operational improvements. We explore four key areas of advancement—namely, particle classification, detection, segmentation, and object tracking—along with a look into the emerging field of augmented microscopy. This paper also underscores the vital role of the existing datasets and implementations that support these applications, which provide essential insights and resources that drive continuous research and development in this fast-evolving field. Our thorough analysis aims to outline the transformative potential of AI-driven PVA in improving precision in future manufacturing at the microscopic scale and thereby preparing the ground for significant technological progress and broad industrial applications in nanomanufacturing, biomanufacturing, and pharmaceutical manufacturing. This exploration not only highlights the advantages of integrating AI into conventional manufacturing processes but also anticipates the rise of next-generation smart manufacturing, which is set to revolutionize industry standards and operational practices.