Given the increasing food needs of humanity and the challenges cultivated species face in adapting to the climatic uncertainties we experience, it is urgent to cultivate new species. A highly relevant repertoire for this purpose is offered by the array of edible wild plants. We analyzed data from Murcia (Spain), involving 61 species and 59 informants, and the Global Database of Wild Food Plants, which includes 15,000 species, 500 localities, and nearly 700 references. Using local consensus, global distribution, and GBIF occurrence data, we built simple unimodal or bimodal models to explore their limitations. Our study highlights that approximately 15,000 wild or feral plant species are consumed as food, underlining the urgent need to support existing crops with new species due to current food crises and climate irregularities. We examined wild plant diversity from a horticultural perspective, considering their relationships with weeds and invasive species. Partial criteria, such as local consensus or global use, were found insufficient for selecting candidate species. We propose developing a specific artificial intelligence to integrate various factors—ecological, nutritional, toxicological, agronomic, biogeographical, ethnobotanical, economic, and physiological—to accurately model a species’ potential for domestication and cultivation. We propose the necessary tools and a protocol for developing this AI-based model.