Sea surface temperature (SST) is a critical factor in the global climate system and plays a key role in many marine processes. Understanding the variability of SST is therefore important for a range of applications, including weather and climate prediction, ocean circulation modeling, and marine resource management. In this study, we use machine learning techniques to analyze SST anomaly (SSTA) data from the Mediterranean Sea over a period of 33 years. The objective is to best explain the temporal variability of the SSTA extremes. These extremes are revealed to be well explained through a non-linear interaction between multi-scale processes. The results contribute to better unveil factors influencing SSTA extremes, and the development of more accurate prediction models.