This article aims to develop a model for Kazakh-language texts exhibiting signs of suicidal behavior. Computational linguistics encompasses natural language processing—that is, natural language processing (NLP). The results of this study will undoubtedly be used to address natural language processing tasks—for example, in the analysis of text corpora, in specific subject domains, in data retrieval systems, in machine translation, and in other areas. The main problem addressed in the article is the prompt detection of suicidal tendencies based on data from open sources using machine learning and deep learning methods. The goal is to develop and compare effective classification models based on the analysis of Kazakh texts. Differences in the adaptation of texts in the state language will be taken into account, including morphological difficulties and the phenomenon of agglutination. The research focuses on developing machine learning models tailored to identify patterns of suicidal behavior in our national language, as well as on examining the interplay between them. Improving the monitoring of online sources using artificial intelligence will enable a rapid response to crisis situations