Volcanic activity may lead to potential volcanic eruptions, but it also provides critical information for understanding the physical processes within a volcanic system. Combining multiple observations and advanced physical models allows us to explore the response of the surrounding host rock to changes in the physical condition in a magmatic system. This work focuses on developing and applying a robust data-model fusion framework to investigate the mechanisms involved in volcanic unrest, such as deformation, failure, and pore fluid migration. First, using a series of tests based on the synthetic data, I optimize a data assimilation technique, Ensemble Kalman Filter (EnKF), to improve its performance in forecasting volcanic unrests with multiple geodetic observations. Then, the robustness of the EnKF is confirmed in application to the unrest and 2009 eruption of Kerinci volcano, Indonesia. To understand the effects of uncertain rheology on our model results, I conduct a systematic sensitivity study to determine the impact of rheology on the host-rock failure prediction. With a better understanding of the uncertainties in my models, I establish numerical models by integrating multiple observations to investigate the magma reservoir dynamics, crustal stress, failure-related seismicity, and hydrological interactions of two different magmatic systems, Laguna del Maule in the Andes, Chile, and Atka in the Aleutian, USA. In both systems, the pre-existing structures and pore fluids play critical roles in catalyzing seismicity, redistributing masses, and delaying/trigger eruptions
