Correct classification of date fruits considering genetic variation is one of the important features of any effective crop management and quality control in agricultural practice. However, the various methods being applied for the purpose often suffer from inaccuracies and can be time-consuming. This challenge is addressed by a machine learning-driven approach that makes use of a rich dataset of date fruits that have markedly different appearance features, along with various algorithms, which classify seven species of date fruits into the categories: Barhee, Deglet Nour, Sukkary, Rotab Mozafati, Ruthana, Safawi, and Sagai based on genetic variations. Automation of the particular process holds great promise for improvement in efficiency, quality checking, and agriculture with reduced manual intervention and human errors. In this work, accuracy by support vector machine and logistic regression has turned out to be 93.33% and 92.8%, respectively. Results of the present study are of immense relevance for improving date fruit classification methods.