Knowledge of information related to land use and land cover in a region is necessary for urbanization projects, sustainable development and natural risk management, particularly floods. The aim of this article is to explore the use of Artificial Intelligence techniques and the combination of multi-sensor images to map land use and land cover in the Marahoué region. To this end, the Deep Forest algorithm is used as the main classifier. Its construction required the use of three common classifiers Extreme Gradient Boosting (XGB), Random Forest (RF) and Extra Tree (ET). Three Deep Forest models (DF-XGB; DF-RF; DF-ET) were developed and optimized to guarantee optimum accuracy. These DF models were then compared with four (04) classifiers commonly used in land use studies (RF, XGB, CNN, CART). The results indicate that the DF-XGB model outperformed all conventional classifiers by over 96%, confirming the relevance of integrated approaches mobilizing multi-sensor data, spectral indices and advanced classifiers. The predominance of cultivated land, the regression of forest formations and the localized presence of wetlands identified by the DF-XGB model, reflect the ongoing dynamics of anthropization. This approach thus offers a powerful tool for environmental monitoring, sustainable community management and flood risk prevention in the Marahoué watershed.