Phosphorus (P) deficiency is one of the most growth‐limiting factors for the productivity of pulses, particularly mungbean (Vigna radiata L.), grown under low input conditions in the semi‐arid and tropical environments. The traditional heavy dependence on phosphoric chemical fertilizers resulted in low phosphorus utilization, excessive phosphate fixation in the soil, and long-term ecological degradation. The first objective of this study is to improve phosphorus sustainability in mungbean crop through the use of eco-friendly inputs (Phosphate Rich Organic Manure, PROM and phosphorus-solubilizing microbial inoculants) based on a mechanistic modeling approach. Secondly, proposed hybrid ANNs were used for predictive modeling and Non-dominated Sorting Genetic Algorithm II (NSGA-II) was applied for multi-objective optimization. The model was developed and validated with experimental data sets for various PROM levels and inoculant treatments by optimizing four agronomic traits (seed yield, straw yield, biological yield, and harvest index). The accuracy of the ANN based model was high (R = 0.923 for seed yield) and NSGA-II algorithm produced optimal input combinations with seed yield higher than 802 kg/ha and biological yield than 2700 kg/ha. This work contributes to the knowledge of the coupling between sustainable agronomic practice, machine learning and evolutionary algorithms to deliver actionable data driven phosphorus management. The current study provides fundamental insights into soil plant microbe interaction and proposes an approach towards a scalable decision-making platform that could be implemented in precision agriculture and biofertilizer industries.