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[Objective] By standardizing, expediting, and automating the evaluation of integrated farmland soil fertility, the present paper aimed to optimize crop planting patterns and make use of land resources rationally. [Method] Taking the tobacco planting areas of Fuchuan Yao Autonomous County as examples, 1038 sample points from Guangxi tobacco planting areas were selected as training data. Based on the consideration of soil conditions, climate environment, and topography, 15 indicators were chosen to calculate the integrated soil fertility index. The Stacking algorithm was applied to establish a regression prediction model for evaluating the integrated fertility of the study area. [Result] The Stacking02 model based on six key factors exhibited excellent performance on the test set, with mean absolute error, mean squared error, and R2 coefficient of determination of 0.0066, 0.0084, and 0.9529, respectively. The Stacking02 model was used to assess the integrated fertility of the study area, showing that the northern part of study area was better than the southern part, with a higher proportion of high-quality production areas. Low comprehensive fertility index regions had relatively high soil pH values and low total nitrogen and available potassium contents. [Conclusion] Compared to primary learners, the performance of the Stacking model improved. The Stacking02 model established using six key factors demonstrated excellent generalization performance, simplifying the calculation process and saving computational power. The same method can also be used to evaluate the soil integrated fertility of the study area. It suggests that this method should be extended to the comprehensive fertility evaluation of other regions and crops’ soils to develop targeted crop utilization schemes. [ABSTRACT FROM AUTHOR]