<p>Landslides in high mountain environments exhibit time-dependent deformation driven by the combined effects of hydrological loading, thermal conditioning, and episodic tectonic forcing. These interacting processes produce evolving slope instability that cannot be adequately represented by conventional static hazard assessments. This study presents a GIS-integrated SBAS-InSAR framework with geo-environmental temporal triggers for spatiotemporal landslide deformation analysis and dynamic risk characterization in the northwestern Himalayas of Pakistan. Surface deformation of three active landslides was analysed using ascending and descending Sentinel-1 images using SBAS-InSAR time series analysis for 2017–2025 years. The two viewing geometries enabled the characterization of deformation patterns while reducing geometric bias. Monthly displacement and velocity time series were systematically compared with rainfall, temperature, and seismic records to quantify the relative influence of triggering mechanisms. The results indicate a clear control hierarchy: rainfall derives sustained deformation and acceleration, temperature acts as a conditioning factor through delayed hydro-thermal processes and seasonal snowmelt while seismicity produces short-lived deformation perturbations. The findings demonstrate the importance of post-seasonal thermal–hydrological interactions in dynamic risk evaluation. Based on these observations, a dynamic risk classification framework was developed that links environmental trigger thresholds with InSAR-derived deformation metrics to quantify monthly variations in landslide risk. The proposed approach captures progressive acceleration, post-seasonal responses, and intermittent instability that are not addressed by traditional static susceptibility or rainfall-only models. This framework provides a transferable, process-based methodology for time-resolved landslide risk assessment in climatically and tectonically complex mountain regions.</p>