 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 ROC curve index based validation and root mean square error, RMSE. Additionally, the Normalized Differential Water Index, NDWI, Normalized Differential Vegetation Index, NDVI, and Normalized Differential Built-Up Index, NDVI, were found to correlate highly with the RF algorithm. This article was authored by Swapantalakdar, Pankajsinga, Susanna Mahato, and others.