Bacterial Blight Disease (BBD) is a significant threat to pomegranate crops, causing severe yield losses and economic setbacks for farmers. Early and accurate detection of BBD is crucial for effective disease management and prevention. Traditional detection methods rely on manual inspection, which is time-consuming, labor-intensive, and often inaccurate. In recent years, Artificial Intelligence (AI)-based approaches, particularly machine learning and deep learning techniques, have emerged as powerful tools for real-time disease detection in agriculture. This systematic review explores the latest advancements in AI-driven BBD detection from pomegranate leaves and fruits, focusing on image processing, computer vision, and sensor-based methodologies. The review synthesizes research findings on datasets, feature extraction techniques, classification algorithms, and performance metrics used for disease identification. Additionally, it highlights the challenges associated with real-time implementation, such as environmental variability, dataset limitations, and computational constraints. Finally, the study discusses future directions, emphasizing the integration of Internet of Things (IoT), edge computing, and hyperspectral imaging for enhanced precision and efficiency. This review serves as a comprehensive resource for researchers and stakeholders aiming to develop robust AI-driven solutions for real-time BBD detection in pomegranate crops.