This appears to be as designed. As seen at https://www.mathworks.com/help/stats/feature-selection-and-feature-transformation.html, the algorithms Chi2, ANOVA, and Kruskal-Wallis produce scores that correspond to -log(p). When the estimated p-value is zero, the score is infinite, or "Inf". A score of "Inf" indicates that, according to that feature ranking algorithm, that feature definitely is useful for determining response class labels. Of course, having lots of features with score "Inf" doesn't provide any discrimination among those features.
A different approach which will provide more discrimination among features is to train some models, and then use model interpretability techniques such as permutation importance or shapley importance to rank the features. The ranking will then depend on factors such as the type of model trained, and the type of model interpretability algorithm that is used. The R2024a Classification Learner app includes Shapley Importance plots.
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