Flat slabs are increasingly popular in modern construction due to their beamless design and ability to optimize space. They help reduce story height and maximize usable floor area. However, accurately predicting the ultimate load capacity of flat slabs is still challenging, influenced by factors such as geometry, materials, and load conditions. This study applies an Artificial Neural Network (ANN) model to predict the ultimate punching shear load of fiber-reinforced concrete slabs, based on 232 experimental data samples. The model consists of four hidden layers and is trained using advanced techniques to enhance generalization capability. Results show that the model achieves high prediction accuracy, with a coefficient of determination R2 = 0.936 and a mean absolute percentage error (MAPE) of 11.88%. These findings demonstrate that ANN is an effective tool for predicting the punching shear capacity of flat slabs.