Scene classification plays a vital role in various computer vision applications, but building deep learning models from scratch is a very time-intensive process. Transfer learning is an excellent classification method using the predefined model. In our proposed work, we introduce a novel method of multimodal feature extraction and a feature selection technique to improve the efficiency of transfer learning in scene classification. We leverage widely used convolutional neural networks (CNN) for feature extraction, followed by relevant feature selection techniques to enhance the performance of the model and increase computational efficiency. In this work, we have executed the proposed method on the Scene dataset of 6 classes and the AID dataset. Experimental results indicate that the MIFS-based approach reduces computational overhead and achieves competitive or superior classification accuracy. The proposed methodology offers a scalable and effective solution for scene classification tasks, with potential applications in real-time recognition and automated systems.