This paper investigates techniques in machine learning applied to species prediction with respect to DNA sequence classification. Most of the model training has been achieved by focusing on feature extraction and model optimization when analyzing public genomic data using min-hashing and k-mer extraction methods to convert DNA sequences into formats suitable for analysis. Then, the study evaluated how well various classifiers (support vector machine (SVM), naive Bayes, random forest) were able to distinguish the species from genetic patterns when combined with other models. This aspect was shown through metrics such as confusion matrix, which were used to assess the performance metrics of the models in terms of high accuracy in species prediction based on DNA sequences. This showcases how a notable incorporation of machine learning approaches in species classification enhances both detail and accuracy and high-resolution while emphasizing the importance of feature engineering in interpreting genomic data. The applicability of these works in evolutionary and biodiversity studies proves their relevance in expanding the field of bioinformatics through a robust framework for classifying DNA-based species.