In this research, neural network and factorial methods were compared for the green synthesis of silver nanoparticles using fig leaf extract. The examined independent variables included temperature, time, stirrer speed, and pH, each assessed at three distinct levels. The experimental design employed the factorial method. The responses analyzed encompassed average particle size, dispersity index, and zeta potential. The results indicated that the smallest average size of silver nanoparticles (25 nm), alongside the lowest dispersion index (0.189) and the highest zeta potential (20.1 mV), was attained at a temperature of 40 °C, a synthesis time of 30 minutes, a stirring speed of 400 rpm, and neutral pH (7). Furthermore, a neural network was utilized to predict the three dependent variables based on the independent variables. The results of the neural network modeling demonstrated high accuracy in predicting the target variables, with average relative error (MRE) values for mean particle size, dispersion index, and zeta potential being 1.99, 0.51, and 74.2, respectively. In contrast, the factorial method yielded MRE values of 2.43, 0.47, and 8.05, highlighting a significant improvement in the prediction of zeta potential and a relative enhancement in the prediction of average particle size. Among the three considered outputs, the accuracy of the ANNs for estimating the average particle size and especially the zeta potential was better than the factorial model. These results of neural network modeling provide great importance in optimizing nanoparticle synthesis processes and facilitating the design of repeated experiments,