Soil erosion is a natural process of the top soil gradual removal which is mainly driven by rainfall, winds and water flows. This process is also influenced by extreme climatic events like prolong droughts or intensive floods which lead to a potential of land degradation. Many research projects have been working on estimating and modeling this soil loss process, such as the USLE (Universal Soil Loss Equation) or its modified RUSLE (the Reverse USLE). Those traditional models, though, offer foundational insights of this process in a certain area, they are often limited by spatial resolution and static parameterization and a large computing volume. This study introduces an integrated framework that leverages freely remote sensed images, artificial intelligence (AI) algorithms and a cloud computing platform—Google Earth Engine (GEE) in predicting soil susceptibility across the study area. Three AI models of different complexities, Random Forest (RF), Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) are examined and trained on key environmental factors. The results have shown that while RF works well in this project for presenting slowly changing erosion risk and no required extra tools, LSTM, which requires highly computing ability, is being expected to be a potentially choice for multi-year and seasonal predictions. SVM, in other word, is ineffective in handling large and complex datasets. Generally, the integration of GEE enhances computational scalability and allows to apply AI algorithms in modeling and predicting environmental and landscape changes.