This work presents an advanced automated system designed to streamline the processing of medical prescriptions. The system follows a multi-stage workflow aimed at enhancing efficiency in handling and interpreting prescription data. The process begins when a user uploads a prescription image, which is then subjected to a series of sophisticated image processing techniques, including grayscale conversion, noise reduction, and morphological operations. These steps are crucial for improving the readability of the text within the image, ensuring that the subsequent stages operate on high-quality data. Once the image is processed, an Optical Character Recognition (OCR) module is employed to extract text from the image. The OCR process involves text region detection, segmentation of lines and characters, and the application of algorithms to recognize and transcribe the text accurately. Following OCR, the extracted text is further refined using language modeling techniques, which involve spell checking, error correction, and the recognition of key entities such as medication names and dosages. To provide meaningful insights, a large language model (LLM) is used to generate a concise and coherent summary of the prescription. This summary distills the essential information from the prescription, making it easier for healthcare professionals and patients to understand and manage the prescribed treatments. The final summary is then returned to the user, completing a seamless, end-to-end process that automates the traditionally manual and error-prone task of interpreting medical prescriptions. This system has significant potential for integration into healthcare management systems, offering a scalable solution for improving prescription processing and patient care.