Followed by the fast advancement of computer science and technology, studies involving massive datasets, especially environmental sciences, have been more accessible for researchers. Yet, with the completion of high-resolution data about the southern Atlantic Ocean region, there has not been sufficient academic research focusing on this specific area. In this regard, this paper investigates the connection between the Sea Surface Temperature (SST) and Sea Surface Salinity (SSS). The researchers chose South Atlantic as the research subject, and based on the visualization of the datasets from year 2010 to year 2014 to determine the number of clusters, the team applied k-means clustering to analyze the relation between SSS and SST by MATLAB. As a result, the research team found that the variation of the value of SSS and SST within the five years showed a stable tendency. The team classified the South Atlantic Ocean into four different oceanographical regions (based on the clusters acquired from the k-means technique), which each area showed a distinct feature in SSS and SST.