Sepsis infection, which is one of the most important causes of death in intensive care units, is seen as a severe global health crisis. If an early diagnosis of sepsis infection cannot be made, and treatment is not started rapidly, septic shock may result in multiple organ failure and death is almost inevitable. Therefore, it is vital to establish an early diagnosis and start the treatment at once. This study aims to accomplish a new model of unsupervised machine learning using lactate and Ph laboratory test values, which are considered to be important parameters to diagnose sepsis infection. The data used in the study have been obtained from MIMIC-III international clinical database. Unsupervised machine learning has been performed via the Fuzzy-C algorithm along with validity indexes like Xie Beni on patients’ data diagnosed sepsis and non-sepsis. The machine-generated ten labels at the end of the training session considering-designed validity indexes. The labelled cluster representatives have been reduced to two dimensions by Principal Component Analysis method in order to monitor the learning in a two-dimensional space. The study contributes to the literature by conducting unsupervised learning through two parameters (Lactate and Ph) and leading to multi-parameter studies. In addition, the study reports that there are five types of sepsis patterns in terms of Lactate and PH laboratory tests.