This analysis model is an innovative way for agricultural decision-making and uses the machine learning approach decision tree regression. Considering the past history of market performance, weather patterns, soil quality indicators, and geographical factors in a long list of variables, this model provides crop yield forecasts with precision and accuracy. This model has been developed using the class Decision Tree Regressor underneath from the scikit-learn library. This gives the model flexibility and scalability. To come up with flexible strategies in cultivation, and reduce risks cautiously. Thus, it forms a deliverable of actionable insight that stakeholders can utilize to raise productivity while improving profitability with sustainable agricultural practices.