Information Communication Technology (ICT) centers play a vital role as essential facilities within our digitalized society. Energy efficiency holds great significance in the ICT sector, driven by the rising energy costs and to reduce the environmental impact. Simultaneously, it is essential to ensure a sufficient cooling supply for servers. Artificial Intelligence (AI) can be used to analyze patterns in large datasets, facilitating valuable insights that are difficult for humans to analyze alone because of the complexity and size of the datasets. The aim of this research is to characterize cooling patterns and explore how AI-driven clustering algorithms can be used to identify cooling operational statuses. The research object is an ICT room situated in Linköping, Sweden, and operated by the global telecommunications company Ericsson AB. The ICT room has Liquid Cooling Packages (LCPs) for water-based cooling.The results show that the average cooling power density in the ICT room is 6.98 kW/m2, and the interquartile range is 8.26 kW/m2. The results also demonstrate the potentialities in using AI-based clustering algorithms, K-means in the presented research, to uncover insights related to cooling operational statuses. Furthermore, the results show that it is suitable to divide the data points into four clusters, providing a detailed description of the characteristics of the dataset. The identified clusters differ with regards to variables, among other, such as LCP return air temperature and temperature difference between chilled water supply and return. This is beneficial in identifying undesired operational statuses of LCPs, e.g., low temperature difference between chilled water supply and return, which is an indicator of a poor cooling performance.