This study introduces a cloud-based License Plate Recognition (LPR) system using computer vision and Easy-OCR. The system entails many applications in traffic management and public safety by identifying and tracking vehicles automatically with their license plates. In this study, we collected 3,500 Bengali car license plate datasets and performed comparative analysis of three YOLO models: YOLOv8n, YOLOv8s, and YOLOv11 for license plate recognition in video streams in video. YOLOv11 had the best performance, achieving the highest mean average precision (mAP) of 98.24% on our collected license plate dataset, while it also demonstrated the capability of real-time object detection for LPR. EasyOCR was used to extract the alphanumeric characters from the recognized plates in rapid moving traffic conditions. Cloud-based communication provided a capacity for no limit to data and processing. The ability to use an operational LPR system with cloud-assisted data dissemination allows more scalability and practical automated LPR applications for traffic management that is transportable anywhere with the connectivity of the internet. The study highlighted the advantages of combining YOLO, OCR, and cloud technologies in a LPR system that can serve law enforcement, increase road safety, and provide smart transport ecosystem solutions within smart cities.