Grazing livestock raises greenhouse gas emissions and contributes significantly to climate change. Excessive grazing also causes soil degradation, makes pastures more prone to drought, and renders them unsuitable for long-term usage. However, well-managed regenerative grazing can help combat global warming without jeopardizing food security and livelihoods for millions of people worldwide. With existing manual procedures, traditional regenerative grazing has proven difficult to apply efficiently. In this paper we propose a novel Artificial Intelligence-powered method for monitoring large-scale regenerative grazing. Using deep learning and publicly available ESA Sentinel-2 satellite images, this method first classifies land cover. Then, using machine learning, 18 bioparameters are tracked, providing farmers with recommendations on how to arrange livestock to minimize environmental impacts. We apply this method to a large conservancy in Kenya. Our case study demonstrates that our artificial intelligence-powered method achieves considerable gains in grass regeneration and offers great promise for improving sustainable development.