Tokenization is the process of dividing a text into smaller parts, called tokens. Tokens can be words, punctuation marks, numbers, or other meaningful elements. Tokenization is primarily used in natural language processing (NLP) and is an essential first step for analyzing, understanding, or inputting text into models. This study implements tokenization for Uzbek at multiple levels: syllable, affix, and word. By conducting a thorough analysis of Uzbek grammar, we developed Python-based programs for each tokenization level. Syllable-level tokenizer adheres to Uzbek phonological rules. Affix-level tokenization leverages the language's agglutinative structure to separate stems and suffixes. For wordlevel tokenization, we compiled and integrated a comprehensive list of separable compound words. The developed toolkit is available as a Python package on PyPI: [https://pypi.org/project/UzbekTokenization]. Our work provides a practical resource for Uzbek language processing and contributes to advancing NLP for low-resource languages.