Remote Sensing and GIS play an important role in mapping and monitoring natural resources and their management. The present study attempts to delineate wetland in the lower Tangon river basin in the Barind flood plain region using suitable water body extraction indices. The main objectives of this present study are mapping and monitoring the flood plains wetlands along with the future status of wetland areas of 2028 and 2038 using the advanced Artificial Neural Network-based Cellular Automata (ANN-CA) model. Apart from wetland area prediction, wetland depth simulation and prediction are also carried out using statistical (Adaptive Exponential Smoothing) as well as advanced machine learning algorithms such as Bagging, Random subspace, Random forest, Support vector machine, etc. for the year 2028. The result shows a remarkable change in the overall wetland area in the upcoming two decades. The small wetland patches away from the master stream are expected to dry out during the forecast period, where the major wetland patches nearer to the master stream with greater depth are rather sustainable but their depth of water may be reduced in the next decades. All models show satisfactory performance for wetland depth mapping, but the Random subspace model was identified as the best-suited depth predicting method and machine learning models explored better results that adaptive exponential smoothing. This recent study will definitely be very helpful for the policymakers for managing wetland landscape as well as the natural environment.