Water is the most important source among the natural sources on the earth's surface for the health of marine and near-shore ecosystems. Assessing water pollution in coastal areas is an essential process for sustainable development. El Gharbia coast, Egypt is one of the most important coasts of Egypt. The main objective of this work is to predict the future changes of water pollution in this coast using Sentinel-2 satellite imagery of three consequent times. First, three Sentinel-2 satellite imagery of consequent dates were acquired and processed for further classification process. The maximum likelihood classification algorithm was then used to prepare the base maps for: time 1, time 2 and time 3, with ten major classes (pollutants). The classified images of time 1 and time 2 were then used to predict the time 3 water pollution map using a Markov Chain Model. After that, the final predicted water pollution map for time 3 was validated with the classified one of the same time. Finally, and compared with the water pollution map of time 3, the future ratios of all types of pollutants have been predicted. The results showed that the proposed model can simulate water pollution changes with reliable results. Based on the simulated water pollution map and by 2030, the ratios of all pollutants will increase. Accordingly, El Gharbia coast and surrounding activities can be saved from more pollution in the future.