This project develops an AI-based crop suitability and recommendation system to support sustainable and climate-resilient agriculture. It combines crop recommendation data, real yield records, and site suitability datasets to build robust models. A dual-model architecture assesses suitability and recommends crops, while machine learning analyzes soil nutrients, pH, temperature, humidity, and rainfall. SHAP-based explainability enhances transparency, enabling users to understand and trust the system’s recommendations.