This study uses intelligent headlights to improve car safety and economy. The suggested system uses Raspberry Pi and Convolutional Neural Networks (CNNs) to dynamically modify beam patterns to real-time ambient circumstances and context. The Raspberry Pi is a flexible computer platform, and CNNs automatically analyze sensor and video data to modify headlight beam patterns. A trained CNN model recognizes impending vehicles, pedestrians, and road conditions. By analyzing this information, the headlights dynamically optimize the beam pattern to maximize visibility and minimize road user glare. This flexibility improves road safety by optimizing lighting when required, minimizing driver fatigue, and enhancing response times. Raspberry Pi allows for a cost-effective and easy retrofitting of current vehicles with intelligent headlights. The system's scalability and adaptability make it suitable for many automotive applications, helping to construct smart and linked transportation systems. This discovery is a major step towards intelligent and adaptable lighting systems that improve vehicle safety and economy.