Early detection of crop diseases is critical for safeguarding food security and improving agricultural productivity. In recent years, the integration of advanced imaging modalities—multispectral and hyperspectral sensors—with artificial intelligence (AI) has enabled unprecedented precision in early disease detection. This paper surveys state-of-the-art research published from 2020 to 2025 on crop disease prediction and detection using AI models trained on multispectral and hyperspectral images. We discuss data acquisition platforms (e.g., UAVs, satellites, ground-based systems), outline major AI architectures (including convolutional neural networks, capsule networks, and physics-informed generative adversarial networks), and highlight both promising results and remaining challenges. Future research directions to enhance early detection and management of crop diseases are proposed.