Change detection using remote sensing data has shown remarkable progress through deep learning approaches, particularly with Convolutional Neural Networks (CNNs) and Transformers, yet both architectures face limitations either in terms of computational efficiency or accuracy. This paper introduces MCD-Mamba (Multimodal Change Detection Mamba), a novel framework that leverages state space modeling for efficient multimodal change detection using both optical and Synthetic Aperture Radar (SAR) data.