The development of automated processing 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 employed in the creation and enhancement of automated systems for the Kazakh language. Through a comprehensive analysis of academic literature, technical reports, and practical implementations, this review identifies key trends, challenges, and advancements in the field. The review highlights the various linguistic complexities unique to the Kazakh language, such as its agglutinative nature, vowel harmony, and rich morphological structure, which present unique challenges for developers. Additionally, the study examines the effectiveness of current tools, including tokenization, part-of-speech tagging, syntactic parsing, and machine translation, in processing Kazakh text. The findings reveal that while substantial progress has been made, there are still significant gaps in the availability and accuracy of these tools, particularly in comparison to those available for more widely spoken languages. The review concludes with recommendations for future research and development, emphasizing the need for more robust datasets, improved algorithms, and collaborative efforts to further advance the field of Kazakh language processing.