In the modern digital era, most information is available in digital or virtual formats that serve a wide range of users. Individuals with visual impairments face difficulty in accessing digital data through technological devices. In order to enhance accessibility for the visually impaired, this study advocates the methods of machine learning approaches such as SVM, KNN, and logistic regression (LR) to facilitate the conversion of Kannada text into Braille. The proposed methodology entails a systematic approach involving tokenization, stemming, feature extraction, model training and testing, culminating in empirical assessments and comparative analyses. This research conducts a comprehensive evaluation of various algorithms to gauge their effectiveness in improving Braille usability for individuals with visual impairments.