Methane (CH4) is the chief contributor to global climate change. Recent advancements in AI-based image processing have paved the way for innovative approaches for the detection of methane using hyper-spectral imagery. Existing methods, while effective, often come with high computational demands and associated costs that can limit their practical applications. Addressing these limitations, we propose the Mamba MethaneMapper (MMM), a cost-effective and efficient AI-driven solution designed to enhance methane detection capabilities in hyper-spectral images. MMM will incorporate two key innovations that collectively improve performance while managing costs. First, we will utilize a gpu-aware state-space encoder, which optimizes the computational resources and efficiency of the system. Second, MMM will use an environment-sensitive module to prioritize image regions likely containing methane emissions, which are then analyzed by our efficient Mamba algorithm. This selective approach not only improves the accuracy of methane detection but also significantly reduces unnecessary computations and memory consumption.