In today’s fast-moving world, consumers rely on packaged foods. This is extremely important for easy access to detailed and personalized nutritional information. This project focuses on developing mobile applications for barcode scanning. This includes extensive food details, including ingredients, nutritional value, allergen warnings, and personalized consumption recommendations based on a person’s health. Applications written with Python and Kivy provide a seamless user experience, allowing individuals to scan barcodes and upload images of ingredients to extract and analyze related information. Additionally, it includes optical character detection (OCR) using Tesseract, which extracts text from photos to allow users to analyze the ingredient list and nutritional name, even if barcode scans are not possible. By taking into account user nutritional limitations or illnesses such as diabetes, lactose intolerance, or gluten sensitivity, this application provides tailor-made health advice and helps individuals make found food decisions appropriately. A secure user authentication system improves the experience by storing your preferences and receiving recommendations created by tailors. The main goal of this project is to enable consumers to choose food in real time and promote healthier consumption habits. The combination of barcode scanning, OCR, and a structured database causes applications to close the gap between the complexity of food indicators and user understanding. Future improvements include mechanically learning-based ingredients, integration into real-time product databases, and expansion of several platforms beyond Android. This initiative represents an important step in using technology to improve consumer health awareness and ensure safer and sounder decisions for food consumption.