The application of soft computing (SC) models for predicting environmental variables is widely gaining popularity, because of their capability to describe complex non-linear processes. The sea surface temperature (SST) is a key quantity in the analysis of sea and ocean systems, due to its relation with water quality, organisms, and hydrological events such as droughts and floods. This paper provides a comprehensive review of the SC model applications for estimating SST over the last two decades. Types of model (based on artificial neural networks, fuzzy logic, or other SC techniques), input variables, data sources, and performance indices are discussed. Existing trends of research in this field are identified, and possible directions for future investigation are suggested.