Hate speech is a form of communication that conveys hostility, rejection, and contempt toward a person or group, based on features such as ethnic origin, religion, or gender identity. Its purpose is to foster an environment of violence and discrimination. In this paper, we present the findings of a pilot test designed to validate the dataset, the proposed methodology, and evaluate the performance of traditional algorithms in detecting ethnic hate speech. The analysis focuses on discriminatory content directed toward indigenous communities in Mexico, classified into three categories: “Hate,” “Non-Hate,” and “Unrelated Hate.” The dataset seeks to fill a gap in hate speech detection studies in Mexican Spanish, which have primarily focused on manifestations of homophobia and misogyny. We implemented traditional machine learning algorithms such as Naive Bayes, Logistic Regression, Multilayer Perceptron, and Support Vector Machine. Additionally, three different vectorizations were considered: TF-IDF, BERT, and ASCII, to identify the best way to extract features. Based on our results, we found that using the Support Vector Machine and BERT vectorization improved the classification of ethnic hate speech.