Rainfall plays a major role in India’s agriculture, economy, water resources, and daily life. Most parts of India depend heavily on the southwest monsoon season for farming and drinking water. However, due to climate change, rising temperatures, El Niño conditions, and rapid urbanization, rainfall patterns have become highly irregular in recent years. Accurate rainfall prediction has therefore become very important for disaster management, agriculture planning, and water conservation.This research paper focuses on the prediction of rainfall in India for the year 2026 using Geographic Information System (GIS), Remote Sensing, and Artificial Intelligence (AI) techniques. The study uses satellite imagery, historical rainfall records, temperature data, vegetation indices, and spatial analysis methods to understand rainfall variability across India. GIS helps in mapping rainfall distribution and drought-prone areas, while AI models improve forecasting accuracy through pattern analysis. Based on recent climate trends and meteorological observations, the study predicts that some regions of India may experience below-normal rainfall during the 2026 monsoon season, whereas coastal and northeastern regions may receive heavy rainfall events.The integration of GIS and AI provides a modern and effective approach for rainfall forecasting and climate resilience planning in India.

Keywords: Rainfall Prediction, GIS, Artificial Intelligence, Indian Monsoon, Remote Sensing, Climate Change, El Niño