This research paper introduces an innovative solution to address the unique challenges encountered by visually impaired individuals in perceiving and navigating their surrounding environment. By leveraging advanced machine learning and deep learning algorithms, a sophisticated object detection and navigation system integrated with GPS technology is developed. Object detection involves identifying different object categories within an image by creating bounding boxes around them and determining their respective categories. Recent progress in the field of object detection has produced accurate models that are built using various algorithms, which have their own merits and demerits. This paper demonstrates real-time object detection using the system camera, along with distance and direction tracking, and voice-guided path navigation through GPS. The model uses several APIs to combine advanced machine learning and CNN architecture. Initially, the visually impaired person will enter the destination where he/she wants to travel. The model utilizes the Google Maps API for walking directions and detects obstacles using object detection. It calculates distances using a coordinate system and indicates the spatial location of objects concerning the camera and the user's position. The system's output is converted into audio feedback for the user. The audio output is achieved using the Google Text-to-Speech API. Hence, the proposed prototype allows a person to “see” through their ears, resulting in an independent life for the individual.