Seismic monitoring is used to ensure the safety of workers in the rock massif. The main security threat is a rockburst, which can be predicted based on the sequence of seismic events. An important task is to develop a mining forecasting model that can take into account the structural heterogeneity of the mountain range and select the necessary forecast horizon depending on monitoring data. In the paper, we propose a flexible approach that combines multiple machine learning models designed to solve various tasks (clustering, time series forecasting) as parts of one composite model. This approach allows for adjustment of the forecast horizon of the model, which enables it to flexibly adapt to rock massifs with different geological structures and seismic monitoring stations. Also, the use of clustering models allows us to take into account the physical and mechanical features of the rockburst formation process. According to experimental results, the resulting composite model showed more accurate results for specific forecast horizons, compared with classical “hierarchical” models and machine learning models. At the same time, the obtained model allows us to interpret the results from the rock mechanics point of view.