K. Pravallika1, R. S. Shankar, S. C. Shekar , P. Prashanth Revolution, big data, AI, crop, diseases, future 10-13 Volume 2 (1) January, 2026 Modern agriculture is undergoing a transformation thanks to the quick development of artificial intelligence (AI) and big data analytics, especially in the area of crop disease prediction. Manual field inspections and laboratory testing are two examples of traditional disease detection techniques that are frequently ineffective and slow. AI-powered solutions minimise agricultural losses and guarantee food security by utilising machine learning algorithms, satellite imaging, IoT sensors, and drone technology to enable early identification and prevention of plant diseases. By optimising pesticide use, encouraging sustainable agricultural methods, and offering real-time analytics, these tools help precision agriculture. However, obstacles like high implementation costs, issues with data quality, and the requirement for farmer training prevent widespread adoption.