This research provides a thorough examination of existing literature and includes basic simulation findings to improve the precision of predicting agricultural yields. The study introduces a novel ensemble model called RSXBoost. RSXBoost combines three robust machine learning algorithms—random forest (RF), support vector machine (SVM), and XGBoost—into a unified framework that utilizes their unique capabilities. The model integrates a range of environmental and soil parameters, including nitrogen, phosphorus, potassium, temperature, and rainfall, by extensively preprocessing historical crop production data. The simulations illustrate that RSXBoost attains a high level of prediction accuracy, as evidenced by the tight correspondence between actual and forecast crop yields, resulting in a tiny margin of error. The examination of feature importance indicates that nitrogen and temperature are the primary contributors, with importance scores of 0.30 and 0.25, respectively. This suggests that they play a crucial role in accurately predicting crop output.