Agriculture plays a vital role in providing food and supporting the economy. However, farmers often face problems like unpredictable weather, poor soil conditions, pest attacks, and limited resources. These factors make it difficult to estimate crop yield accurately and result in losses. Traditionally, farmers rely on their personal experience or basic methods, which are not always reliable in today’s changing environment. This project, Predictive Analysis for Crop Yield, focuses on using machine learning techniques to forecast agricultural production by analyzing data such as weather patterns, soil type, and past yield records. Models like regression, decision trees, and neural networks are applied to reduce errors and provide dependable predictions. The goal is to design a reliable prediction system that helps farmers make better decisions and supports policymakers in ensuring food security.