Physical and optical obstacles hinder safe and independent navigation for individuals with vision impairments. These limitations affect the performance of everyday tasks and also pose a threat to one’s safety while identifying possible dangers, including possible obstacles (objects) around them. This review discusses the current deep learning-based warning systems that assist the blind and visually challenged in navigation and hazard identification. Computer vision methods, with a special emphasis on CNN-based strategies, and YOLO-based models to achieve fast yet accurate object detection, are primary focuses. This study evaluates assistive technologies for the distance estimates of objects, their detection, and real-time voice feedback in case of object classification, distance computation, and auditory alerting. This report further suggests areas of further study and offers proposals for future projects aimed at improving the accuracy, efficacy, and usefulness of innovative technologies.