This research unravels the intricate relationship between soybean seed characteristics, geographical origin, and the resultant quality parameters of tofu. The study analyzed 178 soybean varieties from diverse regions, categorizing them into distinct clusters based on protein, moisture, and other attributes. Significant variations emerged, with soybeans from the United States exhibiting higher protein, while Chinese sources displayed higher moisture content. Subsequently, the research delved into diverse tofu quality parameters using multivariate analysis. Distinct clusters were identified based on attributes including yield, texture, moisture content, and brix levels. These parameters exhibited complex interrelationships, providing insights into factors defining tofu sensory qualities. Furthermore, an innovative integration of Hyperspectral Imaging and machine learning accurately predicted tofu quality categories from soybean seeds with 96-99% precision, revolutionizing conventional assessment methods. The research underscores the multifaceted nature of factors influencing tofu quality, considering seed origin and composition. It highlights the need to tailor soybean sourcing and processing practices to achieve desired textural and sensory attributes aligned with consumer preferences. The pioneering use of advanced technologies sets the foundation for enhanced quality evaluation, improved production practices, and product innovations in the tofu industry.