<![CDATA[This study aims to review methods of artificial intelligence (AI) in land use modelling. Data were extracted from journals in the Scopus and Google Scholar databases using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method. The review demonstrates that modelling land use predictions is a complex matter that involves land use maps and driving forces. AI technology can support land use forecasting by interpreting land use data, analyzing drivers, and modeling. However, AI has limitations in terms of broad contextual understanding and algorithmic errors. To anticipate this, it is necessary to select the appropriate image resolution and interpretation method in accordance with digital data segmentation. It is also recommended to use spatial regression methods to determine the driving forces that affect land use. Hybrid models such as multilayer perceptron neural network Markov chain (MLPNN-MC), random forest algorithm (RFA), and cellular automata (CA)-Markov chain (MC) are recommended for modelling. The selection of a model should be based on the data's characteristics and tested for accuracy. The use of AI for land use prediction modelling is expected to provide accurate predictions that can be used as a basis for land use policy.]]>
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