Smart Traffic Monitoring and Violation Detection with YOLOv8 Algorithm involves leveraging advanced deep learning techniques to automate the identification of motorcycle riders' helmet usage and vehicle license plates in real-time. This technology enhances traffic monitoring and safety enforcement by providing accurate and efficient detection capabilities. The project focuses on developing a robust system for detecting helmets and number plates using the YOLOv8 architecture, which is known for its high accuracy and speed in real-time applications. The system aims to address the increasing number of motorcycle accidents due to non-compliance with helmet usage, emphasizing the importance of safety gear in preventing head injuries. By utilizing YOLOv8, the detection process is optimized for various environmental conditions, ensuring reliable performance even in challenging scenarios such as poor lighting or occlusions. A comprehensive dataset of annotated images is created, featuring diverse scenarios to train the YOLOv8 model effectively. The model is fine-tuned using transfer learning techniques to enhance its detection capabilities for both helmets and number plates. Experimental results indicate that the YOLOv8 model achieves high accuracy rates in detecting helmets and number plates, making it suitable for deployment in intelligent transportation systems.