The Smart Crop and Price Predictor is designed to support agricultural decision-making by combining soil characteristics, environmental parameters, and machine learning techniques. The system processes values such as nitrogen, phosphorus, potassium, temperature, humidity, pH levels, rainfall, and location indicators to generate reliable predictions for suitable crops and their expected prices. The purpose of this project is to create an easy-to-use platform that transforms raw data into useful insights through trained models.The system uses classification methods for crop selection and regression methods for price prediction. Algorithms such as Decision Tree, Random Forest, XGBoost, and AdaBoost are explored to determine their strengths across different feature patterns. Each algorithm is trained and evaluated to ensure stability, accuracy, and consistency in the prediction process. The project emphasizes interpretability, allowing users to input conditions and obtain understandable outputs without complexity.A web interface built using HTML, CSS, and JavaScript serves as the front-end layer, while Flask and Python manage data flow, user authentication, and model integration. Users can register, log in, and obtain predictions through a structured layout. The design supports expanding the feature set, updating datasets, or integrating additional algorithms in the future.The project demonstrates how systematic data handling, model training, and front-end interaction can be combined to form an effective predictive system. It establishes a framework that can be reused or extended for various data-driven tasks in similar domains.Keywords: crop prediction, price estimation, soil analysis, rainfall, pH, nitrogen, temperature, classification, regression, machine learning