Fashion shopping can be particularly challenging for those with visual impairments, as the ability to visually assess clothing is typically crucial. The absence of accessible shopping experiences can result in dependence on others, limiting independence. To address this issue, “Sightless Fashion” introduces an innovative use of deep learning to improve the shopping journey for visually impaired individuals. Deep learning, a branch of artificial intelligence, has made significant strides in fields like computer vision and natural language processing. By harnessing these technologies, we can create systems that interpret visual data and comprehend text, bridging the gap between users and fashion items. This paper proposes a fresh approach that integrates cutting-edge deep learning models to provide visually impaired users with personalized and accessible fashion shopping experiences. The system outlined combines computer vision and natural language processing to enable users to interact with fashion items through non-visual cues like voice commands. By analyzing product descriptions, user preferences, and image characteristics, the system generates customized recommendations, empowering users to explore and select clothing items that suit their unique tastes and needs. This method not only promotes inclusivity in the fashion realm but also enhances independence and empowerment for individuals with visual impairments. The study’s method demonstrates an overall prediction accuracy of 89% for categorizing items and 78% for recognizing patterns. These findings underscore the system’s effectiveness in accurately identifying and suggesting fashion items.