Handwritten character recognition poses a significant challenge in pattern recognition due to the extensive variability in individual handwriting styles. This study focuses on the recognition of handwritten Urdu numerals, which is an essential component of the Urdu script. By harnessing the capabilities of machine learning and deep learning techniques, this research tackles the complex patterns found in handwritten numerals. A specialized dataset with 18,702 samples of individual Urdu numerals was compiled to train a Convolutional Neural Network (CNN) model. The model’s performance was then assessed using a separate test set consisting of 4,676 samples. The CNN model demonstrated exceptional accuracy in numeral recognition, highlighting its effectiveness as a tool for the automated interpretation of handwritten Urdu numerals.