Over the past ten years, a lot of research has been done on automatic vehicle recognition utilizing machine learning approaches. The majority of earlier research on vehicle detection was done on datasets like as GTI, which mostly comprise well-organized road scenarios. These datasets do not accurately depict road scenes in areas with a lesser transportation infrastructure. As a result, the IDD dataset—which represents unstructured traveling situations—includes a large variety of vehicle classes, and shows adequate intra-class variability for vehicles—has been used to study vehicle recognition in this work. The suggested approach uses three neural networks—DenseNet21, SqueezeNet, and EfficientNet-B0—to extract features, which are then concatenated. An SVM classifier that uses a linear kernel has been trained using this fused collection of features. On a portion of the IDD dataset, the proposed methodology yields 89.73% recognition accuracy. In comparison with using the individual neural networks for feature extraction, there is a noted improvement in recognition accuracy of approximately 2.59%. The GTI dataset has also been evaluated, and all models achieved accuracy scores more than 99.4%.