Billing for medicines in healthcare environments is often a manual and error-prone process, especially under high pressure. This research proposes an automated system leveraging Named Entity Recognition (NER) and Optical Character Recognition (OCR) using the Spacy library to address these challenges. The approach involves extracting key details from images of medicine labels through OCR, followed by classifying these details into entities like batch numbers using NER. The identified information is then matched against a pre-populated CSV database to retrieve drug names, prices, and expiration dates. This data is used to automatically generate invoices, significantly reducing manual intervention. The system is designed to handle poorly formatted text and multilingual inputs, making it versatile in diverse healthcare settings. The integration of OCR and NER not only boosts billing efficiency but also improves accuracy compared to traditional methods, paving the way for more advanced automated billing solutions in healthcare.