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Classifying vehicles in real time was necessary to manage and plan road traffic and avoid frequent traffic jams, traffic violations, and fatal traffic accidents. However, detecting vehicles at night presents a significant challenge, requiring the classification algorithm to be tested under diverse conditions, such as rainy weather, cloudy weather, low illumination, and others, which makes identifying vehicles a complicated task. This paper detected and classifiess vehicle through YOLO-v2, ResNet50, and an optimally configured Long Short-Term Memory (LSTM). But figuring out the best hyperparameters by trial and error took longer and was more complicated. The research resolved the computational time and complexity by involving Oppositional-based Wild Horse Optimization (OWHO) techniques to identify the optimal hyperparameters for LSTM. The result showed that the proposed technique was better, with an average accuracy of 97.38% in classifying vehicles, which was better than other techniques. [ABSTRACT FROM AUTHOR]