These models are considered cost-effective and capable of highly accurate output through complex mathematical formulations and precise input data to provide a high performance of models [2]. Various machine learning models such as artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), decision tree (DT), and ensemble techniques such as bagging and boosting are used in susceptibility mapping of floods and landslides [14]. The predictive performance of conventional machine learning models such as ANN, LR, and SVM can be improved by advanced ensemble techniques such as bagging and boosting [14; 17; 18]. Advanced ensemble techniques have gained preference among researchers for susceptibility modeling of floods and landslides. Various tree-based ensemble learning models, such as Random Forest (RF), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost), have been widely employed due to their robust predictive capabilities [7; 13; 19]. [PERSON] et al. [20] conducted flood susceptibility mapping in England using three machine learning models: Classification and Regression Trees (CART), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Among these, XGBoost showed the highest predictive performance, achieving a receiver operating characteristic (ROC) curve value of 0.93. Similarly, [PERSON] [13] evaluated the performance of various machine learning models, including artificial neural networks (ANN), Gradient Boosting Machine (GBM), RF, and support vector machine (SVM), for rainfall-induced landslide susceptibility mapping across three districts in Artvin, Turkiye. The study found that GBM exhibited the highest prediction accuracy, achieving a prediction rate of 97%. Moreover, [PERSON] and [PERSON] [7] applied the XGBoost model for landslide susceptibility mapping in the districts of Trabzon, Rize, and Artvin, Turkiye, highlighting its superior performance from earlier studies while comparing with other machine learning models on landslides, floods, and forest fires. Similarly, [PERSON] et al. [17] conducted a comprehensive assessment of landslide susceptibility by applying the XGBoost model in the upper basin of the Ataturk Dam, Turkiye, achieving an area under the curve (AUC) value of 0.96, indicating high predictive accuracy. So far, previous studies have typically focused on a single type of hazard, using either a single machine learning model or multiple machine learning approaches. The selection of conditioning factors is crucial to obtaining effective prediction models [12,14]. Generally, the topographic (slope aspect, slope, elevation, profile curvature, plan curvature), hydrologic (aridity, topographic wetness index (TWI), moisture index), geologic (soil type, land use), environmental (climate, watershed, drainage pattern, vegetation), and human-related factors (distance to roads, distance to cities, distance to the drainage network) have been major contributions to flood occurrence and landslide formation [12,14,15]. However, [PERSON] and [PERSON] [7] stated that there are no specific criteria for choosing conditioning factors and specific characteristics of the study area, and the data availability plays a crucial role. This study proposed a new unified approach to multi-hazard susceptibility mapping by employing a single machine learning model, XGBoost, based on the same geospatial predictors for both flood and landslide assessment. The analysis was conducted in the Basento River basin, southern Italy, integrating high-resolution topographic, geological, and remote sensing data. The conditioning factors were selected based on the main literature review and data availability, particularly high-resolution satellite imagery and geospatial datasets of the study area used for flood and landslide mapping.