<p>Forest plays a significant part in the terrestrial ecosystem. Early monitoring to prevent interferences before intense and sometimes leads to irreversible canopy conditions due to the dynamic nature of these changes. It is crucial to manage the root cause of many serious canopy health problems. The recent methods such as machine learning techniques and GIS technology are essential in detecting canopy stress levels more accurately than the traditional approach. The spatial and non-spatial data covering the aspect of the health status were considered and GIS-based thematic maps such as Normalized Difference Vegetation Index, Advanced Vegetation Index and Soil Bareness Index were generated. This study demonstrated the applicability of Sentinel 2&#xa0;A data in mapping the health condition of the Sathyamangalam Forest between 2017 and 2024. All the maps were performed through a random forest classifier to generate SFCS canopy model. The final output is a high-resolution, spatially explicit classification map identifying zones of varying canopy stress. The investigation exhibited notable changes in the canopy stress and declining patterns in the plantation endorsed poor vegetative health and higher stress levels over the whole Sathyamangalam forest in 2024. Within the span, there is evidence of extremely strained and stressed conditions at almost 35.28% and 26.44% of the entire region, respectively. This model novels a hybrid geospatial and machine learning framework that reliably predicts forest canopy stress by combining topography and climatic characteristics with multi-temporal satellite-derived indicators to support forest managers and policymakers with a robust tool for ecological monitoring, conservation planning, and climate resilience strategies in biodiversity-rich forest landscapes.</p>