The world is experiencing an unprecedented biodiversity crisis, with nearly one million species at risk of extinction and the rate of loss is accelerating. Conservation efforts are often hindered by limited access to high-quality, location-specific data on ecosystems and species distributions. We demonstrate how high-resolution, open-access data can support sustainable development and biodiversity conservation. Our paper leverages advances in machine-based pattern recognition to estimate species occurrence maps using georeferenced open data from the Global Biodiversity Information Facility (GBIF). We developed occurrence maps for around 600,000 species—including vertebrates, arthropods, mollusks, vascular plants, fungi, and others—using GBIF data. Species ranges were estimated using the “alphahull” algorithm, allowing for flexible, data-driven boundary mapping. We validated our results by comparing them with expert-derived maps from recent literature on mammals, ants, and vascular plants, finding a close similarity in global distribution patterns. We also generated extinction risk indicators based on threat and protection factors, leveraging open high-resolution satellite data. These maps reveal previously unrecognized patterns of biodiversity and extinction risk, particularly among underrepresented species groups. Our findings demonstrate the power of open-access environmental data combined with modern analytical tools to generate actionable insights for biodiversity monitoring. The resulting framework is scalable, replicable, and adaptable to future updates from evolving datasets emphasizing the critical importance of public access to high-resolution environmental data for addressing global challenges.