Test case prioritization is important in regression testing as it enhances testing efficiency by arranging test cases to catch errors quickly. Traditional test case prioritization methods use factors such as code coverage, change information, and historical data to prioritize test cases. Implicit testing can be used to uncover hidden dependencies and user behaviours, leading to the exploration of natural language processing for test case identification. This chapter proposes a novel approach that includes implicit test cases for test case prioritization using natural language processing techniques for feature extraction and classification. Natural language processing is used to analyse test case descriptions to identify implicit test cases, which can then be prioritized alongside explicit test cases. Feature selection is implemented using term frequency–inverse document frequency (TF–IDF) scores, and a multinomial naive Bayes (MNB) classifier is trained to predict labels based on the selected features. Our trained model has an accuracy of 92%.