This study presents a real-time license plate detection system using artificial intelligence (AI) and machine learning (ML) techniques. The system leverages region-based convolutional neural networks (RCNN) and advanced RCNN algorithms to achieve accurate and efficient identification of license plates in dynamic environments. The research optimizes algorithm performance and deploys the system in a cloud-based environment to enhance accessibility and scalability. Through careful design and optimization, the system consistently recognizes license plates with high precision, recall, and computational efficiency. The results demonstrate the system's efficiency and usability in real-world applications, promising to revolutionize automatic vehicle identification. The integration of AI and ML into real-time license plate recognition signifies changes in traffic management, safety assessments, and smart city planning. Interdisciplinary collaboration and continuous innovation are crucial for shaping sustainable and balanced intelligent transportation systems. The system uses RCNN and advanced RCNN algorithms to detect and recognize license plates, achieving unprecedented accuracy and efficiency. The study also highlights the importance of hyperparameter optimization and cloud-based deployment for improving system performance. The results show significant improvements in processing time and detection accuracy with the advanced RCNN algorithm compared to traditional RCNN. The system's performance is evaluated in both on-premise and cloud-based environments, demonstrating the benefits of cloud computing for scalability and efficiency. The research contributes to the development of intelligent transportation systems by providing a robust and efficient real-time license plate detection solution.This study presents a real-time license plate detection system using artificial intelligence (AI) and machine learning (ML) techniques. The system leverages region-based convolutional neural networks (RCNN) and advanced RCNN algorithms to achieve accurate and efficient identification of license plates in dynamic environments. The research optimizes algorithm performance and deploys the system in a cloud-based environment to enhance accessibility and scalability. Through careful design and optimization, the system consistently recognizes license plates with high precision, recall, and computational efficiency. The results demonstrate the system's efficiency and usability in real-world applications, promising to revolutionize automatic vehicle identification. The integration of AI and ML into real-time license plate recognition signifies changes in traffic management, safety assessments, and smart city planning. Interdisciplinary collaboration and continuous innovation are crucial for shaping sustainable and balanced intelligent transportation systems. The system uses RCNN and advanced RCNN algorithms to detect and recognize license plates, achieving unprecedented accuracy and efficiency. The study also highlights the importance of hyperparameter optimization and cloud-based deployment for improving system performance. The results show significant improvements in processing time and detection accuracy with the advanced RCNN algorithm compared to traditional RCNN. The system's performance is evaluated in both on-premise and cloud-based environments, demonstrating the benefits of cloud computing for scalability and efficiency. The research contributes to the development of intelligent transportation systems by providing a robust and efficient real-time license plate detection solution.