Locating and removing landmines and other ERW (Explosive Remnants of War) is dangerous, hazardous, and time-consuming. It requires implementing multilevel on-site surveys: general non-technical surveys to mark the areas affected and technical surveys to determine the perimeter of related minefields. This paper introduces a landmine location-based prediction model, combining military experience with machine-learning techniques and spatiotemporal data, by introducing a new approach for area selection and adding military-based features for context modelling and model training. Besides predicting landmine’s location areas, this model classifies the affected regions by priority and difficulty of clearance, in such a way as to minimise the long time needed by surveys and reduce the danger related to that task, thus providing the clearance organisations with a good resource allocation for their operations. We applied several machine learning techniques that combine Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBOOST), taking into consideration the imbalanced data problem and tweaking for the best performance and accuracy. The experimental results show that the model has the potential to provide reliable predictions and valuable services for demining operations on the field.