This paper explores the use of YOLOv8 algorithm for object detection to identify electric vehicles (EVs) from other conventional diesel/petrol vehicles in real time in urban parking infrastructure. By leveraging a dataset with 1200 high-resolution images, augmented to 3353 images, the model was trained and evaluated under various conditions like diverse lighting, background clutter, and different vehicle orientations. The results showcased the algorithm’s remarkable performance, with a mean average precision (mAP) of 91% and precision and recall rates of 91.8%. These metrics underscore YOLOv8’s validity and accuracy in intricate, real-world scenarios, making it a compelling and powerful tool for optimizing parking facilities and managing EV charging stations. The framework also plays a critical role in developing sustainable urban mobility by effectively assigning dedicated charging stations and facilitating the harmonious integration of EVs into current infrastructure. This paper highlights the rising significance of EV detection in environment friendly urban development, providing insights into practical applications and future research directions to further increase intelligent parking solutions. YOLOv8 provides an encouraging solution to the challenges of real-time EV detection, contributing substantially to sustainable urban planning and transportation systems.