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Visually impaired individuals face significant challenges in recognizing objects, avoiding obstacles, and navigating safely in both indoor and outdoor environments. Traditional assistive tools such as walking sticks and human guidance offer only limited support and lack the ability to provide real-time contextual information about surroundings. To address these limitations, this project proposes an AI-based assistive software system that enhances mobility and independence through intelligent object detection and voice guidance. The system captures live video input using a camera and processes the frames using advanced deep learning models such as YOLOv5 or YOLOv8 to accurately identify objects in real time. Along with detection, the system estimates the approximate distance of objects to determine their proximity and potential risk. Based on this analysis, it generates audio feedback using text-to-speech technology, informing the user about nearby objects and issuing timely warnings to prevent collisions. Additionally, speech-to-text functionality enables users to interact with the system through voice commands, making it fully hands-free and user-friendly. The system can be further enhanced with scene understanding to recognize environments such as roads, staircases, or crowded areas, thereby providing more meaningful navigation assistance. Integration with GPS can support outdoor navigation, while emergency features like SOS alerts can improve user safety. Designed to be cost-effective and portable, the solution can be deployed on laptops, mobile devices, or embedded platforms such as Raspberry Pi 4. With support for multi-language voice output and customizable alerts, the system ensures accessibility for diverse users. Overall, this intelligent assistive solution leverages advancements in computer vision and artificial intelligence to provide real-time environmental awareness, significantly improving the quality of life, safety, and independence of visually impaired individuals. The system is designed with scalability in mind, allowing future integration of additional sensors and modules without major architectural changes. It can be connected to cloud platforms for data storage and performance improvement through continuous learning. The use of edge computing ensures low latency and faster response, which is critical for real-time navigation. Energy efficiency is also considered, making the solution suitable for battery-powered portable devices. The system can adapt to different lighting conditions, ensuring reliable performance during both day and night. Advanced filtering techniques help reduce false detections and improve accuracy