Innovative Research Publication International Journal of Innovative Research in Computer Science and Technology 2347-5552 Volume 13 Issue 2 English March - April 2025 N N Y 2025 04 18 Computer Science Food Safety Prediction System: A Machine Learning Approach to Determining Safe Food Consumption Windows English Y 67 71 K. Satyanarayana Raju English Y P. Yuva Rajesh English N M. Abhishek English N K. Nimshi Babu English N https://doi.org/10.55524/ijircst.2025.13.2.10 This paper presents a novel machine learning-based system for predicting safe food consumption windows. By integrating environmental factors, cooking methods, and storage conditions, our system dynamically estimates food safety durations. Using a Gradient Boosting Regressor model, the system achieves robust performance (with a mean absolute error of approximately ±2.3 hours and an R² score of 0.89) across diverse storage scenarios. In addition, the full-stack implementation—featuring a Next.js frontend and a Flask API backend—facilitates real-time predictions and user-friendly data entry. This approach has significant potential to reduce foodborne illness risks while optimizing storage practices. English Food Safety, Machine Learning, Gradient Boosting, Food Consumption Window, Full-Stack Deployment, Real-Time Prediction. https://ijircst.org/view_abstract.php?title=Food Safety Prediction System: A Machine Learning Approach to Determining Safe Food Consumption Windows&year=2025&vol=13&primary=QVJULTEzNTk=