Machine learning has significantly impacted daily life, with machine translation emerging as a rapidly advancing domain. In healthcare, machine learning presents opportunities for innovation, particularly in translating medical documents into low-resource languages like Tamil. This research develops a transformer-based model fine-tuned for medical terminology translation from English to Tamil. A major challenge was the lack of English-Tamil medical datasets, addressed through innovative data collection methods, such as extracting bilingual subtitles from Tamil YouTube videos. These datasets complement existing resources to enhance model performance. The final model was deployed as a REST API using a Flask-based server, integrated into a React Native mobile application. The app enables users to scan English medical documents, extract text via on-device Optical Character Recognition (OCR), and obtain Tamil translations. By combining advanced natural language processing (NLP) techniques with user-friendly application design, this end-to-end system bridges linguistic gaps in healthcare, providing Tamil-speaking populations with improved access to critical medical information. This study highlights the potential of NLP-driven solutions to address healthcare disparities and demonstrates the feasibility of adapting machine translation systems to specialized domains with resource limitations. The approach also emphasizes scalability for broader applications in similar low-resource settings.