<p>The long-criticized conventional approach, soil test-based unifactorial phosphorus fertilizer recommendation models, faces difficulty finding an efficient and cost-effective fertilization rate. This study aimed to develop optimal phosphorus fertilizer rates for corn using Random Forest Regressor (RF). We built big data comprising 2800 observations with 875 experiments extracted from 85 scientific articles globally, which assessed corn response to phosphorus fertilization. Optimum P-fertilizer rates were derived using the conventional approach: calibration and recommendation. Fertilizer application rate, soil pH, organic matter, texture, fertility class, precipitation, and fertilizer application method were used as input variables to predict ∆Y (Y_{fertilized plot} - Y_{unfertilized plot}) with Random Forest. Response curves obtained from Random Forest predictions were reconstructed with the current unit market prices of corn and phosphate fertilizer to develop the optimal rates. Soil test-based approach showed high uncertainty in estimating the optimum P rate with low R² of 13.1%, 4.3%, and 8.6%, respectively, for P_Olsen, P_Bray−1, and P_Mehlich−3. Prediction of ∆Y with Random Forest gave R² of 84% and 71% for the training and testing dataset, respectively. An example of P_Olsen, P_Bray−1, and P_Mehlich−3 diagnostic systems showed optimal P rates of 21, 24, and 44&#xa0;kg P ha^− 1, respectively, and the multifactorial model predicted nearly identical rates of 22, 25, and 43&#xa0;kg P ha^− 1. In contrast, the unifactorial model recommended very different rates of 13, 40, and 17&#xa0;kg P ha^− 1. The Random Forest model recommended significantly more reliable optimal phosphorus rates for corn than the conventional model based on a simple soil test P.</p>