aspg-28-2341-1792155112 20261016125152 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2023 6 2 Faris H. Rizk Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA Sofia Arkhstan Department of Computer Systems, South Ural State University, 454080 Chelyabinsk, Russia Ahmed Mohamed Zaki Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA Mohamed Ahmed Kandel Department of Architecture, Delta Higher Institute of Engineering and Technology, Mansoura, Egypt S. K. Towfek Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA Traffic detection is critical in ensuring road safety and efficient traffic management, demanding deploying accurate and practical algorithms. This research explores the fusion of Convolutional Neural Networks (CNNs) and the Waterwheel Plant Algorithm to augment global traffic detection capabilities, utilizing a diverse dataset primarily collected from Turkey. A comprehensive evaluation of prominent CNN architectures, such as VGG19Net, AlexNet, ResNet-50, GoogLeNet, and a generic CNN, underscores substantial efficacy, with the CNN achieving an accuracy of 92.14%. Introducing the Waterwheel Plant Algorithm (WWPA) further enhances performance, as exemplified by the hybrid WWPA-CNN model, exhibiting an impressive accuracy of 97.28%. These findings highlight the promising synergies between traditional optimization algorithms and advanced neural networks, showcasing the potential for innovative developments in traffic monitoring systems and broader applications within computer vision. The statistical analyses, encompassing ANOVA and the Wilcoxon Signed Rank Test, robustly underscore the significance of this integrated approach. As the research contributes to the evolution of traffic monitoring systems, these insights provide a solid foundation for advancements in the field, fostering innovation and shaping the future landscape of computer vision applications. 2023 36 45 2341 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.060204 https://www.americaspg.com/journal/28/article/2341 https://www.americaspg.com/storage/01736276842.pdf