Agriculture plays a fundamental role in sustaining the global economy and ensuring food security, yet farmers often rely on intuition and traditional practices for crop selection, leading to inefficiencies in yield and resource utilization. This research proposes a machine learning-based system for smart crop prediction and recommendation, aimed at enhancing precision agriculture through data-driven decision-making. The study integrates historical datasets containing soil parameters (pH, nitrogen, phosphorus, potassium) and climatic factors (temperature, humidity, rainfall) with real-time environmental data fetched via APIs. Multiple machines learning models, including Decision Trees, Support Vector Machines, XGBoost, and Random Forest Classifiers, were evaluated, with the Random Forest model achieving the highest prediction accuracy of 87.93%. A user-friendly Flask web application was developed to allow farmers to input their location and receive real-time crop recommendations. Data preprocessing techniques such as normalization, feature selection, and outlier handling were implemented to improve model performance. Challenges like data imbalance, environmental variability, and the absence of socio-economic factors were acknowledged and addressed where possible. The system's adaptability and scalability make it suitable for diverse agricultural contexts, offering a significant step towards smart farming solutions. Future enhancements will involve the integration of IoT sensors, satellite imagery, and advanced deep learning techniques to further increase prediction reliability and applicability across different regions.