This research work is motivated by the expanding importance of automated digit identification in a variety of applications including, but not limited to, healthcare provider assistance, drug quantity identification in handwritten prescriptions, postal code recognition, banking, and educational assistance. This research emphasizes the importance of data preprocessing, algorithm implementation, and feature engineering in increasing the accuracy and efficiency of digit recognition systems. The work examines the implementation and analysis of a machine learning method for handwritten digit recognition. The proposed comprehensive methodology considers the Modified National Institute of Standards and Technology (MNIST) dataset, which contains a large collection of handwritten digits, and is commonly used to train different image processing systems using machine learning. The Support Vector Machine (SVM) algorithm is proposed for classifying the input digits from the MNIST dataset. Results show that the proposed methodology achieves the following metrics: 94.4% (accuracy), 93.9% (5-folds cross validation), 94.3% (precision), and 94.3% (recall). This research underscores the potential of the SVM algorithm as a robust methodology for digit recognition systems and demonstrates the important role of Artificial Intelligence (AI) algorithms in enhancing system performance and operation.