This article discusses the issue of identifying parts of speech in the Uzbek language with the help of artificial intelligence technologies. The study analyzes morphological complexities, the abundance of affixes, homonymous forms, and context-dependent meanings. It compares the effectiveness of rule-based approaches, machine learning methods, and deep learning neural networks in the automatic detection of parts of speech. The results demonstrate that developing AI-based models for the Uzbek language is a highly relevant and promising direction.