Pomegranate cultivation is a lucrative venture in Asian countries, yet it faces challenges due to fluctuating environmental factors such as temperature, precipitation, and humidity, leading to various diseases that can significantly reduce crop yield. To detect crop diseases in their early stages by monitoring parameters like air temperature, humidity, leaf and soil moisture levels, the system can identify diseases like bacterial blight, fruit spot, fruit rot, and leaf spot, often exacerbated by fungal and bacterial pathogens. This integrated approach enables proactive disease management, helping farmers mitigate losses and maintain the health and productivity of pomegranate plants. The traditional methods of manually detecting diseases in pomegranates are labour-intensive and time-consuming, causing delays in treating diseases at their early stages. This delay leads to quality and quantity deterioration, resulting in significant losses in terms of nutrition, economy, and postharvest for farmers and the country as a whole. Early-stage automatic disease detection is essential to minimize these losses. However, existing disease detection solutions based on digital image processing and machine learning encounter challenges in practical implementation due to suboptimal efficiency and operational complexity. Challenges such as insufficient datasets and the simultaneous consideration of multiple diseases hinder achieving improved performance. The proposed system in this chapter introduces a machine learning-based approach for early detection and classification of two major diseases affecting pomegranates: Fruit rot and Scab. Also the chapter brief about creating a dataset, pre-processing images, segmenting, extracting features, classifying phases.