Dr. N.G.P. Arts and Science College, Kalapatti, Coimbatore, Tamil Nadu, India. (Affiliated to Bharathiar University, Coimbatore) Agricultural productivity in India is highly susceptible to weather variability, with farmers facing challenges like unseasonal rains, droughts, and pest outbreaks linked to climate change. This paper presents a robust, AI-powered weather prediction system tailored for Indian farmers, delivering accurate, localized forecasts to support critical decision-making. Leveraging satellite data, ground-based sensors, and machine learning algorithms, the system provides district-level predictions (5 km resolution) for rainfall, temperature, humidity, and extreme events up to 7 days in advance. Integrated with soil and crop databases, it offers actionable advisories on irrigation scheduling, pest control, and harvest planning via SMS and voice alerts in regional languages (Hindi, Tamil, Telugu, etc.). Field trials across Maharashtra, Punjab, and Karnataka demonstrated a 30% reduction in crop damage and a 25% improvement in water efficiency. The system’s scalability and interoperability with government platforms like Digital Agriculture and PM-KAYAKSHI can significantly boost climate resilience in Indian agriculture.