We studied the specifics of the management of innovative climate-smart agriculture with the use of information and communication technologies (including machine learning). Carbon landfills (or carbon farms) adopt responsibilities in the sphere of achievement of the Sustainable Development Goals as to the reduction of CO2 emissions into the atmosphere. These activities allow regions, territories, and countries to reach their goals in the sphere of CO2 reduction and support for food security. The considered experience of the USA and China showed that the modern stage of nature use does not involve the intensity of development, but it is primarily aimed at the stability of profitability, yield, and saving biodiversity on land and under water. The goal of this paper was to find the features of implementing reconstructive nature use in the sphere of carbon landfills and climate-smart agriculture with the use of machine learning. The scientific novelty of the paper is due to the identification and description of modern trends of formation of reconstructive nature use, which is based on machine learning, and finding the prospects for their adaptation in developing countries.