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Accurate Crop Yield Prediction Is Crucial For Sustainable Agricultural Planning, Food Security, And Resource Optimization. Traditional Prediction Methods Often Rely On Historical Records And Basic Statistical Techniques, Which May Fail To Capture The Complex Interactions Among Factors Such As Soil Quality, Climate Conditions, Rainfall, Temperature, Fertilizer Usage, And Crop Management Practices. With The Rapid Growth Of Agricultural Data, Data Mining Techniques Have Emerged As Effective Tools For Analyzing Large Datasets And Extracting Meaningful Patterns To Support Decision-making. This Study Explores The Application Of Data Mining Techniques For Crop Yield Prediction, Focusing On Methods Such As Regression Analysis, Classification Algorithms, And Clustering Approaches. By Integrating Multiple Environmental And Agricultural Parameters, The Proposed System Aims To Improve Prediction Accuracy, Provide Actionable Insights For Farmers, And Assist Policymakers In Resource Allocation. The Results Demonstrate That Data Mining–based Models Can Effectively Forecast Crop Yields, Enabling Proactive Measures To Enhance Productivity And Reduce Risks Associated With Agricultural Uncertainties.