Agriculture is one of the most significant sectors contributing to global food security and economic development. Increasing climate variability, irregular rainfall patterns, changing temperature conditions, and inefficient utilization of agricultural resources have made crop yield prediction a challenging task for farmers and policymakers. Traditional prediction methods primarily depend on historical observations and manual estimation, which often result in inaccurate forecasts and delayed decision-making. Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) have enabled the development of intelligent systems capable of analyzing agricultural data and generating accurate yield predictions using data-driven approaches.

This research presents the design and implementation of a Machine Learning-based crop yield prediction system that assists in sustainable agricultural planning. The proposed system utilizes agricultural parameters such as rainfall, temperature, and fertilizer usage to estimate crop yield through supervised learning techniques. Linear Regression has been employed as the predictive algorithm due to its computational efficiency, interpretability, and effectiveness in modeling relationships between environmental variables and crop productivity.

The agricultural dataset undergoes preprocessing procedures including data cleaning, feature selection, train-test splitting, and feature scaling before model training. The predictive model is developed using Python and Scikit-learn, while Flask is employed to create a web-based interface that allows users to enter agricultural parameters and obtain real-time crop yield predictions. The performance of the model is evaluated using standard regression metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R² Score). Graphical visualizations such as actual versus predicted values, residual analysis, and feature relationship plots are generated to assess model performance and prediction reliability.

The developed application demonstrates that Machine Learning can significantly improve agricultural decision-making by providing reliable crop yield estimations based on environmental conditions. The proposed system offers an affordable, scalable, and user-friendly solution that can assist farmers, agricultural researchers, and government agencies in optimizing farming practices, improving resource utilization, reducing production risks, and enhancing sustainable agricultural development. Furthermore, the system establishes a foundation for future integration of advanced Machine Learning models, Internet of Things (IoT) sensors, satellite imagery, weather forecasting services, and real-time agricultural analytics to achieve higher prediction accuracy and intelligent precision farming.

Keywords: Sustainable Agriculture, Machine Learning, Crop Yield Prediction, Linear Regression, Predictive Analytics, Artificial Intelligence, Flask Framework, Python, Agricultural Data Analysis, Precision Farming.