Crop Yield prediction is of great importance to global food production. Policy makers rely on accurate predictions to make timely import and export decisions to strengthen national food security. The main goal of this study is to find out how bee species composition and weather affect blueberry yield and to predict optimal bee species composition and weather conditions that achieve the best yield using computer simulation data and machine learning algorithms. Multiple linear regression (MLR), Decision trees Regressor (DTR), Random forest (RF), and Gradient boosting (GB) were evaluated as predictive tools. The techniques and models we will use on predicting Wild Blueberry Yield can also be used on other crops Yield prediction. So, this is the main motivation for working on this project.