Sea surface salinity (SSS) in the eastern tropical Pacific Ocean significantly influences the process of sea-air interactions and exhibits a strong response during the analysis of the El Niño-Southern Oscillation (ENSO). Recently, satellites have provided long-term SSS data, and deep learning methods can achieve spatial-temporal forecasts. We developed a satellite-data-driven deep neural network (DNN) model to achieve reasonable forecasts of SSS fields associated with the ENSO using a series of past satellite SSS data. Our model achieved short- to medium-term forecasts for SSS fields from 6 to 96 days, with an error of less than 0.2 pss. Consistent with the Climate Change Initiative (CCI) SSS Anomaly (SSSA), the SSSA appears approximately 4 months earlier than the filtered Sea Surface Temperature Anomaly (SSTA) during ENSO events. Moreover, the SSSA index forecasted by the DNN also showed strong negative relationship with the Niño3.4 SST index during ENSO events.