The increasing demand for date fruits, due to their nutritional and medicinal benefits, necessitates efficient classification methods to ensure high-quality production. Traditional manual classification, relying on human expertise, often struggles with consistency and time-consuming processes. This study introduces a novel 1D Convolutional Neural Network (1DCNN) specifically designed to date fruit classification with high accuracy. Our model employs multiple convolutional layers, followed by dense layers, incorporating batch normalization and dropout to enhance training stability and generalization. Through meticulous hyperparameter optimization, the 1DCNN surpasses existing methods, achieving a remarkable classification accuracy of 93.89%. Notably, our approach effectively addresses challenges associated with dataset imbalance, demonstrating robust performance across various date fruit varieties. This innovative solution offers a significant improvement in classification efficiency and consistency, contributing to the advancement of date fruit production and quality control.