This paper highlights the aim of creating an effective lane detection system using the powerful image processing tool known as OpenCV. This model provides a safe and reliable solution for autonomous and driver-assisted vehicles, addressing the critical need for accurate lane detection in real time. The research focuses on implementing lane detection by processing images or videos as input, ensuring that the system can operate in different dynamic environments with various conditions. The model used in our research seeks to build a seamless and robust solution to enhance road safety, particularly in tough challenging scenarios such as rain, wind, snow, and other complex driving conditions. These challenges make lane detection difficult, but the system is designed to adapt to these conditions and ensure reliable performance. By using techniques such as edge detection, region of interest masking, and the Hough Line Transform, the model ensures precise identification of lane lines under different conditions. The proposed solution demonstrates adaptability across diverse road types and conditions, contributing to the development of intelligent transportation systems. This study also provides a foundation for integrating lane detection with advanced driver-assistance systems in order to improve the safety and reliability of autonomous vehicle navigation.