The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project is to improve how discarded textiles are sorted, enabling more clothing to be reused, resold, or used as secondary source of raw materials. Today, most textile sorting is done manually, which limits how much material can be processed and leads to valuable garments being discarded. This project will develop artificial intelligence (AI) tools that help identify the material composition and resale potential of textiles more quickly and accurately. By increasing the efficiency of textile sorting, this technology can increase the use of such materials, and support the growth of the circular economy. It also has the potential to create higher-quality jobs in sorting facilities by shifting work from manual inspection to technology-assisted operations. This project aligns with national priorities around resource efficiency and domestic manufacturing, and advances economic outcomes. The project addresses a major challenge of developing a scalable, AI-driven system for textile classification that can accurately predict both resale and potential for being secondary source of raw materials under real-world conditions. The primary innovation lies in combining hyperspectral and Red-Green-Blue (RGB) imaging with multimodal machine learning to enable automated sorting decisions across heterogeneous textile streams, a task that is difficult to replicate due to the need for large, high-quality,