<p>This study proposes a novel predictive approach for constructing landslide susceptibility maps by combining boosting models with hyperparameters optimized using Optuna. We used a well-defined spatial database comprising 128 landslides and 22 factors affecting landslides in the Eunsan village, Buyeo City, Chungcheong Province, Korea. Optuna is an open-source hyperparameter optimization framework for automating the determination of the best hyperparameters for machine learning (ML) models. The results demonstrate the excellent performance of three ML models optimized using Optuna. The LightGBM model exhibited the highest accuracy (a receiver operating characteristic value of 0.932) among the three models, followed by the XGBoost model (0.926) and the random forest model (0.917). These ML models highlight the significance of hyperparameter tuning and selection using Optuna. The efficiency, ease of use, and flexibility of Optuna make it a powerful tool for optimizing ML models and accelerating the research and development process in the field of ML and artificial intelligence. This study emphasizes the potential of these approaches as optimized tools for landslide susceptibility mapping.</p>