Mental health has become increasingly recognized as crucial to public health and well-being globally, especially in Indonesia, the world’s fourth most populated country. The Indonesia-National Mental Health Survey (I-NAMHS) suggests that one in three young adults in Indonesia suffers from some form of mental health issues, making mental health issues a concern that needs to be addressed. This research uses a clustering algorithm to effectively predict and identify potential topics among Indonesian users from three mental health groups on Facebook. Additionally, this research follows the Knowledge Discovery in Databases or the KDD process, which involves data selection, preprocessing, transformation, mining, and interpretation/evaluation. In the data mining phase, this research uses the K-means clustering algorithm combined with Word2Vec to satisfy the algorithm’s result. The findings showed that the cluster generated produced a Silhouette Index score of 0.5058, indicating well-structured clusters and a Calinski–Harabasz Index score of 11895.70, indicating a good separation between clusters. The research found four key topics: (1) psychosomatics and early treatment of stress, (2) meaning-seeking and alternative coping strategies, (3) Existential concerns intertwined with religious and social pressures, and (4) emotional expression and relationship difficulties.