Handwritten digit recognition is used in computer vision and pattern recognition. This research categorizes MNIST digits using multiple neural network architectures. A typical machine learning benchmark is the MNIST dataset, which contains 28×28 grayscale photos of the numbers 0–9. Our major objective is to develop and test digit recognition models. Five network models, including an FCN and an ANN, were built, deployed, and assessed in this research. CNN Sequence with Dense Layers Sequential CNN with Convolution and MaxPooling, Flatten, and Dense Layers are alternative methods. These models extract characteristics from pictures using fully connected, convolutional, and pooling layers. We discuss loading, normalizing, and rearranging the dataset. The layers, activation functions, and output requirements of each model are specified in its design. Compilation involves selecting loss functions and optimizers. The validation set evaluates model performance, whereas the training set fine-tunes them. The results suggest that our models recognize digits. Other models have accuracy ranges from 93.70% to 97.92%, whereas Sequential CNN with Convolution and MaxPooling reaches 98.96%. This research focuses on model design, layer structure, and hyperparameter tuning for categorization.