This paper presents a systematic analysis of deep learning-based applications developed between 2019 and 2024 that assist individuals with visual impairments. The study focuses on implementations across domains such as navigation, object detection, and specialized assistance. The methodology combines quantitative performance analysis with validated user studies. An examination of 12 significant implementations reveals key trends in the field of assistive technology. The performance analysis demonstrates that YOLOv8 achieves an 80% precision rate and 68.2% recall, with mAP@.5 of 75.8% for outdoor obstacle detection while specialized architectures like LYTNetV2 reach a 96% accuracy level in traffic light detection. Furthermore, motion tracking implementations utilizing optical flow attain real-time performance with a 90% accuracy in trajectory prediction. Traditional YOLO models, including YOLOv3, achieve 73.3% accuracy in real-time object detection. The analysis unveils an emerging trend toward hybrid hardware solutions that combine smartphones with edge processing units, enabling privacy-preserving local computation while maintaining real-time performance.