The development of new techniques, such as machine learning (ML), can provide better insight into the processes and drivers of soil erosion and runoff. However, the performance of these techniques to assess soil erosion in agricultural landscapes is poorly understood. The aim of this study was to evaluate the performance of four machine learning algorithms, generalized linear model (GLM), Random Forest (RF), elastic net regression (EN) and multiple adaptive regression splines (MARS), in predicting soil erosion and runoff in Syria. Soil erosion and runoff were measured on three experimental plots (2.25 m  1.50 m  0.50 m, 0.10 m depth in the soil), combined with three different slopes and land use types: RS (8%, olive), SS (12%, citrus), KS (20%, pomegranate). Both erosion and runoff were determined after rainfall events of >10 mm between October 2019 and April 2020. Based on 24 effective rainfall events, the average soil erosion was 0.18 ± 0.14 kg m À2 per event in KS, 0.14 ± 0.11 kg m À2 per event in SS, and 0.12 ± 0.10 kg/m 2 per event in RS. Regression analysis indicated strong relationship between the rainfalls and the runoff, the highest connection was recorded in the KS plot (r 2 = 0.85; p < 0.05 n = 24). The analysis of covariance indicated that only the runoff had a significant impact on soil erosion