Leukemia, one of the most frequent cancers affecting all age groups, is defined by the rapid development of abnormal blood cells. With the current trend of personalized medicine, predicting the risk of leukemia based on gene expression is a necessary solution for supporting each patient’s treatment plan. This study evaluates the effects of the T-test-based feature selection technique on improving the performance of machine learning in acute leukemia classification tasks on Deoxyribonucleic acid (DNA) microarrays gene expression data. First, a data pre-processing process is deployed by conducting a data cleaning step and performing a data normalization step with StandarScaler. Next, the feature selection process is employed using the T-test-based feature selection technique to reduce the dimensionality of the gene expression data by choosing the most important genes. Finally, various machine learning models are applied, namely Naïve Bayes, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, and Random Forest, to classify acute leukemia. The experimental results show that the proposed method achieves better performance than the state-of-the-art studies for leukemia classification on Deoxyribonucleic acid (DNA) microarray gene expression data.