<p>Accurate lithological mapping in semi-arid and geologically complex regions is crucial for advancing geoscientific understanding and guiding mineral exploration, yet it remains a technically challenging task. Here, we introduce the Inception-LSTM Hyperspectral Mapper (IL-HS), a deep learning framework designed to enhance lithological classification from hyperspectral satellite imagery. The model integrates InceptionV2 for multi-scale spatial feature extraction with a bidirectional long short-term memory (Bi-LSTM) module to capture sequential spectral information. Using EnMAP hyperspectral data over the Kerdous inlier, Anti-Atlas, Morocco, IL-HS achieved an overall accuracy (OA) of 98.05% across twenty-six lithological units, substantially outperforming state-of-the-art models, e.g., support vector machines (OA = 91.17%) and 3D convolutional neural networks (OA = 94.61%). The proposed model demonstrated strong class-wise performance, including perfect recall for copper and manganese-bearing formations in the Proterozoic and infra-Cambrian basement-cover border mineralizations, and reliably distinguished spectrally similar formations such as multi-age carbonates and volcanic units. These results show that IL-HS effectively mitigates spectral redundancy and mixing artifacts in heterogeneous extended age and altered terrains. Our findings pinpoint IL-HS as a robust and scalable approach for hyperspectral lithological classification and mapping using hyperspectral satellite data, with broad potential for applications in geoscientific research, mineral resource assessment, and sustainable exploration research.</p>