Biochar is widely used to mitigate nitrogen (N) emissions in global croplands. However, its effectiveness varies due to spatial disparities in external factors such as soil properties and climate conditions, as well as biochar characteristics such as pH and carbon content. In this study, we used a molecular model to assess the distinct impacts of biochar and soil on soil N emissions. We employed a back-propagation neural network optimized using a genetic algorithm (GA-BPNN) to simulate N emissions in global croplands, utilizing data from 351 peer-reviewed papers. Then, a global biochar application strategy aimed at optimizing the reduction of N emissions across global croplands was devised by aligning biochar and soil parameters. Our findings indicate that the high electrophilic and nucleophilic properties of biochar's reactive surface significantly contribute to the reduction of soil N emissions. The GA-BPNN-based machine learning (ML) technology demonstrated superior predictive performance (with R-2 ranging from 0.47 to 0.69) in predicting changes in soil N emissions post-biochar application compared to other machine learning algorithms. Our simulations show that optimized global biochar application increases NH3 volatilization but achieves the most significant reduction in global cropland N emissions, amounting to 16.04 Tg N yr(-1) and representing approximately 28.45% of the estimated total N emissions from global croplands, all while preserving crop yields. Therefore, aligning biochar properties with specific soil parameters and environmental conditions could be a promising strategy for mitigating N emissions in global croplands and addressing climate degradation.