<p>Mineral resource zoning employs scientific methods to categorize regions based on resource distribution and development potential, guiding the spatial layout of mines and regulating the intensity of exploitation. Existing studies often use administrative districts as the unit. However, they have frequently overlooked the unique differences between mines and the environmental conflicts that arise. A collaborative zoning method was developed to reconcile resource development with environmental sustainability, thereby determining priority areas for mineral exploitation. A zoning index system was established based on criteria such as resource base, ecological environment conditions, and techno-economic conditions. The “ecological environment conditions” criterion included sub-criteria ecological function importance, ecosystem sensitivity, and ecological environment impact. A hesitant fuzzy linguistic aggregation operator calculated the evaluation value of the linguistic index. Production rules were employed to categorize the exploited mines as historical cases. The target mines were zoned using case-based reasoning with factor weighting by the machine learning algorithm. By applying the method to copper resources in Jiangxi Province, China, the priority sequence for mineral resource development was determined. The results show that the zoning method combines rule-based classification with improved case-based reasoning algorithms, which effectively apply the decision-makers' expertise and the historical laws discovered by machine learning to the spatial layout of mineral resource exploitation. This study provides a decision-support tool for sustainable mineral resource planning and management while balancing resource development and environmental protection.</p>