In today’s urban development, Earth Pressure Balance (EPB) Tunnel Boring Machines (TBMs) play a vital role. It’s crucial to design a comprehensive monitoring system to control surface settlement and prevent damage to surface structures. This study focuses on creating prediction models for estimating ground surface settlement. Two soft computing techniques, namely ANN-CFB and ANN-BP, were used for this purpose. The models were validated using operational data from the Qom metro Line A, specifically the section between A14 and A10 stations. Additional input parameters were incorporated using an image processing approach to include soil properties for each segment. As a result, the most accurate ANN technique was employed to predict ground surface settlements for the mentioned project. The correlation coefficients for training, testing, validation, and the overall result were found to be 0.99439, 0.97873, 0.96381, and 0.98824, respectively. Through sensitivity analysis, the study explored the connections between different parameters and ground surface settlement. The outcomes reveal strong agreement between predicted values and real data. Notably, the parameter ‘cutter head torque’ exhibited the highest impact on surface settlement (8.48%), while ‘Pressiometric Modulus (Ep)’ had the least impact (4.24%).