Cyclone detection is a critical task in meteorology for minimising the impact of extreme weather events. Traditional approaches rely on satellite observations and numerical weather prediction models, which often lack efficiency in real-time detection. In this study, we propose MN-Net, a stacked ensemble deep learning framework that integrates MobileNet and NASNet architectures for cyclone image classification. The model leverages transfer learning and feature-level fusion to enhance classification performance. We provide the community with a large dataset of labelled images of cyclones and non-cyclones and additionally augment our training set with data augmentation techniques to enhance the network’s ability to generalise. Our experimental results highlight the supremacy of MN-Net in terms of accuracy, precision, recall and F1-score over strong baseline models. The overall architecture is suitable for integration into early warning systems for timely cyclone detection and warning dissemination.