Mangrove ecosystems, vital for coastal biodiversity and protection, confront escalating degradation from human and natural influences. Addressing the imperative for precise degradation assessment, this study introduces a Random Forest-based technique, utilizing NDVI (Normalized Difference Vegetation Index) feature extraction within a spatio-temporal framework. The principal aim is to establish a robust approach for evaluating mangrove degradation and land cover shifts. This involves extracting NDVI values from satellite images to monitor vegetation health and changes chronologically. Leveraging the Random Forest algorithm, acknowledged for managing intricate relationships and classifications, further enhances the methodology.By situating the approach spatio-temporally, degradation patterns and alterations in mangrove distribution are traced over time. The temporal progression of the study area is considered, affording a thorough degradation analysis. Outcomes affirm the method's efficacy, evidenced by a Cohen's Kappa Score of 0.96 denoting substantial agreement between predictions and observations. Remarkably high scores across accuracy, precision, recall, and F1-score (all at 0.97) underscore the model's precision in classifying mangrove degradation levels. The amalgamation of the Random Forest-based approach and NDVI feature extraction emerges as a valuable instrument for precise mangrove degradation assessment. The spatio-temporal analysis augments comprehension of degradation dynamics, pivotal for proficient mangrove management and conservation strategies.