The Indian Sundarban has the unique mangrove region and dynamic deltaic ecosystem zone that experience the ever-changing land use and land cover pattern (LULC) due to the environmental and man-made interactions. The LULC pattern assessment is very adequate for monitoring the biodiversity, ecological integrity, resource management in sustainable way as well as climate change impacts. The current chapters aimed to prepare the LULC map using multi-temporal satellite images through the Support Vector Machine (SVM) method to identify the change pattern of land use in Sundarban. The chapter has been identifying the change pattern of Indian Sundarban land use over 20 years by preparing the LULC high resolution map. The prepared LULC map accuracy has been checked by the Kappa Co-efficient method. The SVM classifier, known for its reliability to manage non-linear boundaries and very limited training data, exploring the high classification accuracy, validated using Kappa statistics and confusion matrix. Results demarked adequate spatiotemporal modifications, including degradation mangrove vegetation and expansion of settlements, and emphasize the need for sustainable coastal land management policies. This chapter exploring the potential of SVM-based classification for accurate LULC mapping in ecologically sensitive and susceptible coastal regions like the Sundarbans.