The study outlines the creation of a groundbreaking application that implements Augmented Reality (AR) and Artificial Intelligence (AI) to identify parts of the hybrid propulsion system in the Toyota RAV4 hybrid. This research aimed primarily to develop an educational resource enabling technicians and students to grasp and explore hybrid propulsion systems without needing physical parts, utilizing interactive virtual models that can be accessed via mobile devices. Central to the system is a convolutional neural network (CNN) that has been trained using the YOLOv8 algorithm, enabling precise identification of vehicle components. A public dataset was used for model training, ensuring the results are valid and can be compared with earlier research. Furthermore, the model has been exported in ONNX format, which makes integration with platforms like Unity easier, thereby creating a smooth and engaging AR experience. The application developed illustrates how merging AR and AI can revolutionize technical education, allowing for more effective and accessible learning. With AR, users can see and learn about the components of hybrid propulsion systems in realtime, which enhances their comprehension of these intricate systems. Feedback from users indicated a high level of satisfaction regarding the interface, detection accuracy, and overall learning utility of the tool. This research shows the promise of AR and AI to enhance technical education in the automotive sector, and it holds potential for adaptation in other vehicle models or various fields, providing a scalable and effective method for training in both educational and professional settings.