The lack of precise disease detection systems in date palm farming within the New Valley Governorate hinders timely interventions, leading to increased pesticide use, and significant productivity losses. This research introduces a novel approach integrating remote sensing data processing, GIS techniques, and Normalized Difference Vegetation Index (NDVI) analysis for early and accurate detection of date palm diseases. The proposed cartographic model is designed to map and visualize the spatial distribution of NDVI across date palm fields to identify areas with potential plant stress. Using multispectral and multi-temporal Landsat imagery, we processed near-infrared and red bands. The resulting NDVI data were integrated into a modular environment within GIS software. The model is structured into two main steps: normalization of the near-infrared and red bands and the production of digital cartographic maps visualizing the distribution of NDVI. The results revealed a significant correlation between spatial variations in NDVI and the vertical distribution of date palm areas, with lower NDVI values indicating potential plant health issues. To evaluate the model's reliability, an accuracy assessment analysis was conducted, using metrics such as Kappa coefficient, confirming the robustness and performance of the method. The developed method will facilitate enhanced disease management and contribute to more effective crop health and productivity strategies, as it allows for identification of the locations with possible plant diseases early on, which can help in reducing pesticide usage and improve date palm production. This study is specifically designed to detect common date palm diseases such as [leaf spot diseases targeted].