Handwritten digit recognition is a critical job in computer vision and is used as a frequent benchmark for testing machine learning algorithms. This work describes the creation of a recognition system utilizing the K-Nearest Neighbors (KNN) method, which was chosen for its simplicity and ease of understanding. The system is built on the MNIST dataset, which contains a vast number of photographs of handwritten digits. The process begins with data collection and investigation, which involves analyzing the content and properties of the MNIST dataset to better comprehend the range of handwriting styles. This is followed by data preparation, which consists of standardizing pixel values to increase algorithm performance and dividing the dataset into training and testing sets. This split aids in assessing the model's generalization capabilities. KNN was selected as the model due to its non-parametric nature, meaning it does not make assumptions about the data's underlying distribution. This characteristic makes it versatile and effective for classification tasks like digit recognition. The model is trained in a unique way compared to other machine learning methods: instead of learning parameters, KNN stores all the training data and uses it during the prediction phase to classify new data points based on the nearest neighbors. The performance of the model was evaluated using accuracy as a metric, which measures the percentage of correctly classified digits. Different values of kkk, the number of neighbors, were tested to find the optimal setting. The results showed that KNN could effectively classify handwritten digits with significant accuracy. Finally, the paper discusses future work and deployment considerations. It highlights the potential for deploying KNN-based digit recognition systems in real-world applications and suggests further research to refine preprocessing techniques, optimize model parameters, and explore more advanced models, such as neural networks, to enhance accuracy and efficiency. This study demonstrates the practical applicability of KNN in digit recognition and outlines a path for future improvements.