Accurate monitoring and forecasting of tropical cyclones is essential to reduce their devastating impacts on vulnerable coastal regions. This study presents an approach using convolutional neural network (CNN) that uses deep learning to detect and classify cyclones from remote sensing images. It uses a custom designed CNN architecture; the model effectively captures the distinct features between cyclone activity and normal weather conditions. The network architecture consists of convolutional layers that extract high-level semantic features which is followed by a fully connected layer for final classification. Performance evaluation is conducted through accuracy measurements and confusion matrix analysis. The proposed CNN model is novel compared to existing methods for its ability to retrieve meaningful contextual information (high-level features) from remotely sensing images, effectively distinguishing cyclone activity from normal weather patterns, offering improved accuracy (92.5%) in detection compared to traditional approaches. Despite challenges such as limited labelled storm data, the model shows strong potential to improve storm detection in different regions. This research helps in advancing deep learning applications in disaster risk management, providing a valuable tool to enhance storm monitoring and preparedness efforts.