Land subsidence is an environmental phenomenon that involves gradual or sudden settlement of the land surface because of compaction of underground material. Groundwater withdrawal, which occurs due to excessive use of water resources, is among the most important reasons for this phenomenon. Therefore, land subsidence can lead to destructive results in residential, industrial and agricultural areas. As a result, subsidence caused by excessive use of groundwater resources has occurred in many countries in the world. Tehran metropolitan plain in Iran is one of the most obvious examples, where land subsidence is happening. Although the relationship between land subsidence, groundwater level decline and changes in the physical properties of subsurface material is broadly understood, a comprehensive and precise model to predict land subsidence remains unconstrained. Land subsidence modeling is a complicated matter in geological engineering but can help to better understand subsidence and possibly prevent damages. The commonly used numerical methods for modeling land subsidence are generally based on simple assumptions, which make the model results to be associated with some errors. In this study, artificial intelligent methods such as Artificial Neural Networks (ANN) were used to propose a new method to predict land subsidence. The efficiency of this method was then tested in the South Tehran plain as a case study. We have used hydrological, geotechnical, remote sensing and ambient vibrations for site effect investigations. First, the collected data was studied statistically. Then, the delay between groundwater withdrawal and subsidence was computed by genetic algorithms using available hydrographs and GPS data in a period of 27 months. Model input parameters include changes in groundwater level, natural frequency of soil, alluvial thickness, defined geographic coordinates and time. The model output was an estimated subsidence measured by radar interferometry method. The model was built in 15 time steps using a set of data having 4 months of time difference with the data used to create the model. The comparison between the predicted (modeled) and real (measured by remote sensing) subsidence shows a good correlation, which makes the proposed model reliable.