The objective of this study performed in the Abidjan District is to map land cover units using the Google Earth Engine (GEE) platform and Machine Learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART), Naive Bayes (NB), and Minimum Distance (MD). The data used include optical Multispectral Sentinel 2A satellite images with a 10-meter resolution, a 12.5-meter Alos Polsar digital terrain model (DTM) resampled to a 10-meter resolution, as well as cartographic data. The implemented methodology starts with the preprocessing and normalization of the composite image. The final composite image is created using eight spectral indices: NDVI, NDWI, MNDWI, VARI, SBI, SAVI, GCI, RGR, along with the first three bands of Principal Component Analysis and slope information. Subsequently, training and validation points are collected and coded based on image reflectance and ground truth data. The different classifiers SVM, RF, CART, MD, and ND are then trained and evaluated using various metrics such as confusion matrix, overall accuracy, producer’s accuracy, consumer’s accuracy (reliability), and Kappa coefficient. The classification performed with the RF algorithm achieved the highest overall accuracy of 83.28%, with a Kappa coefficient of 0.78. The statistics reveal that the Abidjan District is composed of 28.07% urban areas, 25.35% agricultural and other cultivated areas, 12.39% oil palm plantations, 10.05% rubber plantations, 4.66% banana plantations, 2.53% forests, 3.96% mangroves, 3.80% forest plantations (reforestation), and 9.2% water bodies in 2020. This study has led to an improved mapping of the distribution and proportions of land cover classes in the Abidjan District.