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Aim/Purpose The amount of information published has increased dramatically due to the information explosion. The issue of managing information as it expands at this rate lies in the development of information extraction technology that can turn unstructured data into organized data that is understandable and controllable by computers Background The primary goal of named entity recognition (NER) is to extract named entities from amorphous materials and place them in pre-defined semantic classes. Methodology In our work, we analyze various machine learning algorithms and implement KNN which has been widely used in machine learning and remains one of the most popular methods to classify data. Contribution To the researchers' best knowledge, no published study has presented Named entity recognition for the Kikuyu language using a machine learning algorithm. This research will fill this gap by recognizing entities in the Kikuyu language. Findings An evaluation was done by testing precision, recall, and F-measure. The experiment results demonstrate that using K-NN is effective in classification performance. Recommendations for Researchers With enough training data, researchers could perform an experiment and check the learning curve with accuracy that compares to state of art NER. Impact on Society NER helps recognize important textual components including names of individuals, places, and monetary value among others. When dealing with enormous datasets, it is critical to sort unstructured data and to find vital information by identifying the major entities in a text. Future Research Future studies may be done using unsupervised and semi-supervised learning algorithms for other resource-scarce languages. [ABSTRACT FROM AUTHOR]