A Deep Learning Framework for Handwritten Digit Recognition: From Pixels to Predictions finds use in everything from bank check processing to postal automation. The performance of three machine learning models—CNN, Random Forest Classifier, and KNN—is investigated and contrasted in this work using the popular MNIST dataset as well as a bespoke dataset consisting of 500 handwritten digit images per class. Deep learning is better at capturing spatial hierarchies, as evidenced by the suggested CNN-based sequential model, which outperformed Random Forest (93.2%) and KNN (96.8%) to attain the maximum accuracy (98.5%). To improve input quality, preprocessing methods such as OpenCV-based noise reduction and normalization were used. Each model’s workflow included data collection, preprocessing, feature extraction, and classification. Accuracy, confusion matrices, and F1-scores were used as evaluation metrics. CNN’s automatic feature extraction worked best for digit identification, even though Random Forest gave resilience against overfitting and KNN offered simplicity. This study emphasizes the trade-offs between accuracy and computing efficiency across approaches and underlines the promise of deep learning in real-time digit categorization.