Abstract
The development of automated systems for the Kazakh language has gained significant momentum in recent years, driven by the growing need for natural language processing (NLP) tools tailored to underrepresented languages. This systematic review aims to critically evaluate the existing observational tools and methodologies used in building and improving automated systems for learning the Kazakh language. Through a comprehensive analysisof scientific literature, technical reports, and practical implementations, this review identifies key trends, challenges, and advances in the field. The review highlights various linguistic complexities unique to the Kazakh language, such as its agglutinative nature, vowel harmony, and rich morphological structure, which pose unique challenges to developers. Additionally, the study examines the effectiveness of modern tools, including tokenization, part-of-speech tagging, parsing, and machine translation,in processing Kazakh text. The findings show that despite significant progress, there are still significant gaps in the availability and accuracy of these tools, especially when compared to those available for more widely spoken languages. The review concludes with recommendations for future research and development, highlighting the need for more robust datasets, improved algorithms, and collaborative efforts to further advance Kazakh language data science.