Pomegranate is an important fruit crop with high nutritional and economic value. However, its production is significantly affected by diseases such as bacterial blight, anthracnose, fruit rot, and leaf spot. These diseases reduce both the quality and quantity of the produce, leading to economic losses for farmers. Early detection is therefore essential to control their spread and improve productivity. Traditional methods rely on manual inspection, which is time-consuming, labor-intensive, and often less accurate. This study presents an automated system for detecting pomegranate disease using image processing and machine learning. The main objective is to develop an efficient and accurate method for identifying diseases in fruits and leaves at an early stage, while ensuring the system remains simple and practical for farmers. The proposed system includes four main stages: preprocessing, segmentation, feature extraction, and classification. In preprocessing, images are resized and enhanced to improve quality and remove noise. Segmentation is performed using K-means clustering to separate infected regions from healthy areas. In the feature extraction stage, key features such as color, texture, and shape are obtained from the images. These features are then used in the classification stage, where a Support Vector Machine (SVM) classifier is applied to distinguish between healthy and diseased samples. The system is trained and tested on a dataset of pomegranate fruit and leaf images, achieving high accuracy in disease detection. This demonstrates the effectiveness and reliability of the proposed approach. Overall, the developed system offers a fast, accurate, and practical solution for early disease detection in pomegranate crops. It helps reduce crop losses, improve yield quality, and supports the adoption of smart and sustainable agricultural practices.