The cracking tolerance index (CT_index) from the indirect tensile cracking test (IDEAL-CT) is widely used to assess the asphalt mixture cracking resistance. Although informative, CT_index encompasses interrelated parameters and alone may not fully capture the cracking behavior. This study applies the principal component analysis (PCA) to explore relationships between IDEAL-CT parameters and their influence on cracking resistance. To this aim, a total of 165 samples from 33 plant-produced asphalt mixtures were tested using the IDEAL-CT. The PCA revealed two principal components capturing significant data variance; PC1 was mainly influenced by displacement and slope at 75 % post-peak load (l₇₅ and m₇₅), whereas PC2 associated highly with fracture energy. In addition, K-means clustering classified four distinct mixture groups with varying CT_index values and load–displacement characteristics. Cluster analysis revealed relationships between IDEAL-CT parameters and mix composition, including asphalt content, binder modification, reclaimed asphalt content, and mix type. Findings indicated that the study successfully demonstrates the potential of using PCA and clustering to provide deeper insights and a more comprehensive framework for understanding the asphalt mixture cracking behavior beyond reliance on CT_index alone. Results introduced a relationship between load–displacement curves as IDEAL-CT output and different asphalt mix compositions.