The recovery of nitrogen and phosphorus from simulated wastewater in the form of struvite was investigated through a Machine Learning (ML)-based approach. The Extreme Gradient Boosting Algorithm (XGBoost) and Random Forest (RF) models were used for single-objective and multi-objective prediction of the recovery rates of N and P, respectively. The effects of seven process conditions on struvite crystallization were identified. The results showed that XGBoost outperformed RF in both single-objective (R^2=0.91~0.93) and multi-objective (R^2=0.89) predictions. Furthermore, experimental validation was conducted with initial phosphorus concentrations of 10 mg/L and 1000 mg/L to determine the optimized process conditions for struvite recovery using the multi-objective model. The optimal conditions were found to be: N∶P ratio of 1.2∶1, Mg∶P ratio of 1∶1, pH of 9.5, reaction time of 80 min, reaction temperature of 25 ℃, and stirring rate of 240 r/min.