Plant diseases significantly impact global food security, causing yield losses and reduced crop quality. Traditional detection methods are labor-intensive, slow, and hindering timely interventions. This chapter explores how AI, deep learning, and IoT technologies can revolutionize plant disease detection in sustainable agriculture. A review of recent research and experiments shows that convolutional neural networks (CNN), transfer learning models like ResNet and EfficientNet, and multispectral imaging can all be used to classify crops like rice, maize, and wheat with 90% to 98% accuracy. The integration of IoT sensors and cloud-based platforms enables real-time monitoring, early diagnosis, and optimized pesticide use, fostering sustainable practices. AI-driven disease detection helps make better predictions about crop yields, has less of an effect on the environment, and makes it easier to make decisions based on data. The chapter emphasizes the need for understandable AI systems and accessible tools for farmers to ensure equitable benefits and digital transformation in agriculture.