Loneliness among college students is an increasingly prevalent issue. While technology-based methods for detection using behavioural patterns have been proposed, there remains an opportunity for improvement as insufficient attention has been given to the individual behavioral differences among students. Loneliness is a highly subjective experience and people’s routines differ, making it a challenge for generic models to accurately determine its presence and severity. In response to this challenge, it is particularly helpful to identify subgroups within the population that exhibit similar behavioral characteristics, enabling a more nuanced understanding and detection of loneliness. This paper introduces a novel approach to loneliness detection, leveraging a data set gathered through passive sensing using mobile phones, which provides a rich source of user behavioral data. We utilized unsupervised clustering to find subgroups of students exhibiting similar behavioral patterns over time within the data set. This approach is essential for continuous monitoring, identifying changes in behavioral patterns, and facilitating the early detection of loneliness. Using data from 41 students’ smartphones, we created group-specific classification models to identify loneliness. Group-based prediction models for loneliness detection have shown significant improvement in accuracy over generalized models. These findings can lead to the development of more effective, tailored methods for loneliness detection in diverse populations. This study emphasizes the importance of personalized approaches in mental health interventions and highlights the potential of passive sensing data in creating tailored loneliness detection methods.