 This paper reviews six machine learning algorithms used for land use slash land cover, LULC, classification. The random forest, RF, algorithm was found to be the most accurate, with a CAPA coefficient of 0.89. This result is supported by the receiver operating characteristic ROCE, curve, index-based validation and root mean square error, RMSE. Additionally, the RF algorithm had the highest correlation with normalized differential vegetation index NDVI, normalized differential built-up index NDVI, and normalized differential water index NDWI. These results suggest that the RF algorithm is the most suitable for LULC classification. This article was authored by Swapantalitar, Pankaj Singha, Sussata Mahato, and others.