The adoption of Artificial Intelligence (AI) in agriculture has transformed plant disease management by providing accurate, real-time, and scalable solutions. This chapter provides a recent overview of AI-based methodologies, which cover deep learning architectures such as CNNs, transfer learning approaches like ResNet50 and InceptionV2, and hybrid methodologies that encompass DWT, PCA, and GLCM. Highlighted outcomes include disease classification accuracy rates of 99.97%, 94.1%, and 98.43% with multimodal chatbot assistance. Ensemble classifier-based yield prediction models (e.g., Inception ResNet V2, Random Forest, Logistic Regression) were as accurate as 91%. These innovations not only improve disease detection and yield prediction but also enhance sustainability through minimizing input wastage and maximizing resource utilization. The chapter emphasizes the importance of AI’s contribution to agriculture in ensuring long-term food security and resilience, particularly in data-poor or resource-limited settings. The results highlight the necessity of continued innovation and deployment of AI products in the agriculture sector.