If we see, annually mountainous regions are famous for their challenging and hazardous driving conditions, which are characterized by sharp turns, steep inclines, and the frequent presence of obstacles such as animals, humans, and other vehicles. This research paper presents a noble approach to enhancing safety on ghat roads by deploying a real-time computer vision-based system that will help in reducing these risks. Utilizing the YOLOv8 (You Only Look Once) as the object detection algorithm, the system identifies and classifies potential hazards from camera-captured images. Simultaneously, OpenCV-based lane detection algorithms provide good guidance to the driver by analysing roads and their curves. Integrating these technologies this paper aims to offer immediate and precise alerts and directional instructions to drivers and thus, significantly reducing the risk of accidents at such blind turns. After several experimental demonstrations, the system's effectiveness in various environmental conditions highlights its potential as a robust solution for accident prevention on steep blind turns. This approach promises to enhance road safety, offering a proactive measure to protect drivers, passengers, and wildlife.