The objective of this paper is to classify the Date palm disease in two categories first one for date palm leaves and second is for date fruit. With the technical aspect correlation computation over the features analysis is major task to be cover in this paper. Date palms are vital agricultural crops with significant economic and cultural importance in many regions. However, their productivity is frequently at risk from a variety of diseases that may severely impact yield and fruit quality 30–40% crop losses per year so this research is dedicated to diagnosis and grouping of date palm diseases by analyzing key features extracted from images and other Pertinent information through cutting-edge approaches in ML, involving feature extraction and classification of computational processes, with correlation factor of features. The planned system is meant to accurately detect and categorize common diseases affecting date palms. The approach enhances early diagnosis, enabling timely intervention and improved crop management. Results from experiments confirm the model's effectiveness in delivering accurate outcomes, providing a reliable tool Enabling agricultural professionals and farmers to monitor and protect date palm plantations.