This article discusses the methods of morphological, syntactic, and semantic analysis used in natural language processing for the Uzbek language. The linguistic features of Uzbek – complex agglutinative morphology, free word order, and limited resources – necessitate a specialized approach and research in applying these methods [Senuma, Aizawa 2017, 100-109]. Within the framework of this study, morphological analysis methods, followed by syntactic and semantic analysis methods, were examined based on scientific sources. Each section presents the existing advantages and disadvantages, experiences in applying these methods to the Uzbek language, as well as comparative analyses with foreign languages. For morphological analysis in Uzbek, rule-based methods, statistical models (HMM, CRF, etc.), and neural network-based approaches (BiLSTM-CRF, seq2seq) are discussed, with results provided in examples and percentages. It is demonstrated that syntactic parsing is carried out using dependency and constituency parsing methods. The issue of constructing a UD treebank for the Uzbek language, which follows the SOV word order, has been examined. The impact of complex morphological structure and free word order in sentences on parser construction is highlighted. As a result of the studied approaches, the issue of building hybrid parsers, integrating them with morphological analysis, and feeding grammatical categories of words into the parser has been raised. Additionally, the development of neural constituency parsers based on neural networks and the effectiveness of their results were analyzed. For the subsequent analysis stage related to NLP, particularly in semantic and sentiment analysis, models ranging from Word2Vec and FastText, which represent word meanings in vector form, to context-adapted transformer models such as BERT, mBERT, and UzBERT were also discussed. The text also considers issues of high-level semantic role labeling and the creation of semantic networks such as WordNet and FrameNet. In evaluating NLP approaches for the Uzbek language, Uzbek is compared with other languages: English, Turkish, and Russian. The application of rule-based, statistical, and neural methods in these languages is examined, and the results of the analysis are presented.