In an environment, one of the natural geological hazards is land surface subsidence. There are several reasons for land subsidence among them are underground coal mining and coal fire in subsurface or deformation is primarily measured in terms of change in ground elevation values ( Z-dimension) at different time intervals at identified ground locations. All the conventional and exiting techniques have certain limitations in monitoring and predicting land surface subsidence. In this work, we predict the land subsidence for one year in the interval of twelve days on the datasets collected through a monitoring technique called Modified PSInSAR. The sample datasets contains 14 locations and 67 previous land subsidence value calculated from each location. We train and test predictive models and perform the prediction of the land subsidence using Vanilla and Stacked long short-term memories (LSTMs). Finally, we demonstrates the predicted deformation values of the 14 locations for one year.