A new approach of encoding with help of Virtual Keypad Letter Substitutions has shown in the improved result of text classification. In this study, we focus on a text document classification dataset comprising 2225 documents distributed across five categories: Politics, sports technology entertainment business. However, using both of these vectors, we initially performed traditional machine learning models like Naive Bayes, Logistic Regression, SVM, and Random Forest over the dataset, which provided us with reasonable accuracy, precision, recall, and F1-Score. However, it is hypothesized that the proposed approach, which uses the encoding technique, Virtual Keypad Letter Substitutions, would improve the performance of these models. The encoding method simply converts the letters in the text data with symbols imprinted on a virtual keypad to enhance abstraction that might better capture such features of the text as semantically and syntactically. These findings attest that the models we propose exhibit massive enhancements in all the metrics under study when trained with encoded data. For example, in Naive Bayes, after encoding the datasets into new features, they recorded an accuracy of 95.14%, precision 95.16%, recall 95.14% and F1-score of 95.12% excluding, it revealed inferior performance to that of raw data. The same effects were observed in other models like: Logistic Regression, SVM, Random Forests; Their accuracies were increased by 28,5% to 41,8%. Based on these findings, the authors recommend the Virtual Keypad Letter Substitution encoding algorithm not only as a tool for increasing the accuracy of text classification but also as a tool for data preprocessing in general machine learning. This method is expected to be advantageous in situations where text data comprises of associated formats or noisy data as the encoding may assist in filtering the most appropriate feature for classification. This work provides helpful information for enhancing the dependent variable associated with each type of the predetermined ML model, including C-SVM and naive-bayes for document classification, although its findings are promising for various disciplines, including NLP, Information Retrieval, and Document Classification, where efficient and accurate text classification is crucial for data-driven decision-making.