The proper categorization of date fruits based on their genetic variations is a crucial component of effective crop management and quality control in the field of agriculture. Nonetheless, the present methodologies utilized for this intention are frequently susceptible to imprecisions and have the potential to consume considerable amounts of time. This study uses images to categorize seven types of date fruit – Barhee, Deglet Nour, Sukkary, Rotab Mozafati, Ruthana, Safawi, and Sagai – based on their genetic variations. This model was particularly developed to detect the different kinds of date fruits utilizing a pioneering dataset including region-specific photos, it is capable of categorizing practically all readily available date fruits. When compared to average precision, accuracy, recall, and F-s, MobileNetV2 outperforms innovative algorithms like AlexNet and VGG16, with scores of 98.55%, 99.508%, 99.45%, 99.47%, and 99.878%, respectively. The investigation's outcomes have significant implications for improving techniques to classify date fruit accurately.