We advance an adaptable framework that couples statistical ecology with deep learning to recognize and predict biosignature spatial patterns in a polyextreme terrestrial environment. Drone flight imagery connected simulated HiRISE imagery to ground surveys, spectroscopy and biosignature mapping to reveal predictable distributions linked to environmental factors. AI/ML models successfully identified geologic features with high probabilities for containing biosignatures at spatial scales relevant to rover-based astrobiology exploration. Targeted approaches augmented by deep learning delivered 56.9-87.5% probabilities of biosignature detection versus <10% for random searches and reduced the physical search space by 85-97%. Libraries of biosignature distributions, detection probabilities, predictive models and search roadmaps for many terrestrial environments will standardize analog science research, enabling agnostic comparisons at all scales. This is vital to preparing, informing and optimizing biosignature quests on Mars, assisting high-stakes mission decisions between competing targets, and maximizing precise selection of high-priority samples. In extreme environments, the distribution of biosignatures is tightly controlled by a complex interdependency of geological, physicochemical, and biological interactions 1-5 . In such environments, microbial populations often occur in non-random spatial distributions closely tied to their