This study employed two artificial intelligence (AI) methods called the ANFIS–FCM algorithm as a novel computational method and an artificial neural network (ANN) as a conventional computational method in order to predict the environmental impacts of soybean production in different scenarios (i.e., soybean cultivation after rapeseed (R-S), wheat (W-S), and fallow (F-S)). The 175 data of life cycle assessment (LCA) method were collected from soybean farms. The two methods called the adaptive neuro-fuzzy inference based on fuzzy C-means clustering algorithm (FCM) and the artificial neural network (ANN) were adopted to predict environmental parameters. For this purpose, the life cycle of soybean production was assessed in terms of environmental impacts through the IMPACT2002+ method in SimaPro. According to the results, the production of each ton of soybean in the defined scenarios resulted in 0.0009 to 0.0016 DALY, 5476.18 to 8799.80 MJ primary, 1033.68 to 1840.70 PDF×m2×yr, and 563.55 to 880.61 kg CO2-eq damage to human health, resources, ecosystem quality, and climate change, respectively. Moreover, the weighted analysis indicated that various soybean production scenarios led to 293.87–503.73 mPt damage to the environment in which the R-S scenario had the best environmental performance. Notably, the emissions caused by the production and application of the diesel fuel followed by chemical fertilizers, particularly N and P fertilizers, were recognized as the most important environmental hotspots in soybean production. According to the results, the ANFIS–FCM algorithm acted as the best prediction model of environmental indicators for soybean cultivation in all cases related to the ANN. The RMSE and MAPE values obtained from ANFIS–FCM were lower than the values obtained from the ANN model for all environmental indicators. For the ANFIS–FCM and ANN algorithms, R2 ranged between 0.9967 to 0.9989 and 0.9269 to 0.9870, respectively. It can be concluded that the proposed ANFIS–FCM model is an efficient technique for obtaining accurate environmental prediction parameters of soybean cultivation.