The paper presents a comprehensive system tailored to address the unique challenges faced by individuals with visual impairments, with a focus on object detection. The proposed system integrates three primary components: dimensionality reduction with unstructured data, an algorithm for object preference determination, and the utilization of the YOLOv8 model for object identification. Additionally, Python-based text-to-speech functionality is incorporated to provide auditory feedback to the user, ensuring accessibility and usability. A key aspect of the system’s functionality lies in the utilization of dimensionality reduction techniques to effectively manage unstructured data, thereby simplifying complex information to enhance comprehensibility. The system employs autoencoder neural networks for this purpose, drawing inspiration from the human brain’s information-processing mechanisms. Autoencoders specialize in condensing high-dimensional data into a more streamlined format with fewer dimensions, akin to transforming a detailed picture into a simplified sketch while retaining the essential features. By streamlining sensory input into a lower-dimensional representation, the system enhances its capacity to understand and process diverse and complex sensory information effectively, improving its adaptability to different environments and enhancing accuracy in providing feedback to the user. Through the integration of autoencoder neural networks for dimensionality reduction, the proposed system offers promising potential to assist blind individuals in navigating their surroundings with increased efficacy and independence.