This document presents a neural network model for designing one-way reinforced concrete slabs. The model was developed using 100 training examples covering different design parameters. A feedforward neural network with two hidden layers of 10 neurons each was trained to predict the slab depth, main reinforcement spacing, and distribution reinforcement spacing given inputs of span length, live load, concrete grade, steel grade, and reinforcement diameters. The trained model was validated on 25 additional examples and was able to accurately predict slab designs meeting code requirements without being given those examples during training. The model provides an alternative method for easily designing one-way slabs compared to traditional manual calculations.