Mineral science is rapidly evolving toward automated and intelligent characterization systems capable of supporting exploration, mining, and geo-materials analysis. In this context, we introduce Mycelial_Net, a novel deep learning framework inspired by the adaptive connectivity of fungal mycelium networks. Unlike conventional CNNs, Mycelial_Net continuously restructures its internal topology during training, expanding or pruning artificial synaptic pathways based on entropy and classification performance. This biologically inspired plasticity enables the model to preserve previously learned mineralogical representations while optimizing its structure for new information, mimicking the self-organization of living networks in natural environments. Applied to thin section microscopy images, Mycelial_Net integrated with a ResNet backbone demonstrates superior resilience to noise, low-resolution data, and complex textures. In benchmark tests on a data set of images of mineral thin sections, the architecture achieved validation accuracies exceeding 95%, outperforming CNNs, ensemble learning, and Vision Transformer approaches. These results show the potential of adaptive architectures to provide more consistent and interpretable mineral classification, even under heterogeneous geological conditions. Current research aims to extend this approach toward more challenging mineral assemblages, multimodal petrographic datasets, and industrial applications, such as real-time classification in ore processing environments. The long-term vision is the development of a self-aware mineralogical foundation model capable of autonomous learning, uncertainty quantification, and continuous improvement based on new geological evidence. This work highlights how cross-fertilization between biology-inspired computation, mineralogy, and artificial intelligence can open unprecedented perspectives in digital petrography and automated geoscience—placing Mycelial_Net among the emerging frontiers of Mineral Science.