Accurate crop yield prediction is important to minimize uncertainty for informed decision- making and resource allocation. A variety of machine learning models are used in yield prediction; however, the available benchmarking literature offers limited insight to achieve a balance between predictive accuracy and model interpretability of different models. Therefore, this study was conducted to evaluates popular machine learning models for U.S. soybean yield prediction using a multi-source spatiotemporal dataset comprising weather, soil, and management features. The model performance was evaluated using root mean squared error (RMSE) metric, and feature impact was explained using Shapley Additive Explanations (SHAP) for interpretability. The findings indicate that Random Forest is the best model that achieved least RMSE of 5.07 and highest correlation coefficient of 90.36% on test set. SHAP results revealed that precipitation and solar radiation are leading yield determinants, while soil properties, such as soil pH and bulk density, exerted moderate effects. The contribution of this work is fourfold: (i) a rigorous benchmarking of ML models using accuracy metrics for yield prediction, (ii) evidence based affirming the model superiority for complex agronomic dataset, (iii) systematic assessment of global feature importance connecting yield affecting climatic and edaphic factors, and (iv) application of SHAP as a means for interpretation and explainability. The results bring together predictive performance and explanation, providing insights into the advancement of smart agriculture through informed decision-making for irrigation planning, efficient input application, and climate-resilient strategy formulation.