Climate change increasingly threatens global agriculture through rising carbon dioxide (CO₂) emissions, temperature anomalies, and irregular rainfall. Accurate crop yield prediction is therefore essential for ensuring food security and effective adaptation planning. This study systematically compares three machine learning models—Multiple Linear Regression (MLR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—for predicting crop yields using an extensive, multi-country dataset with climate and soil variables. We introduce a robust preprocessing pipeline that includes Gaussian noise-based augmentation, anomaly-based feature engineering, and dual normalization strategies to improve model generalisability under climate stress. Performance is assessed across different training sizes (70/30 and 80/20 train-test splits) and hyperparameter configurations. XGBoost consistently outperforms the other models, achieving the lowest MSE (0.3841) and the highest R2 (0.6186) thanks to its ability to model nonlinear climate-yield interactions effectively. Key insights include (1) aridity index and temperature anomalies as dominant predictors, (2) water management and crop rotation as effective adaptation strategies, and (3) preprocessing as crucial for model robustness. This work presents a scalable and interpretable framework for applying machine learning to climate-resilient agriculture.