Rashvand, MahdiRen, YuqiaoSun, Da-WenSenge, JuliaKrupitzer, ChristianFadiji, TobiMiró, Marta SanzoShenfield, AlexWatson, Nicholas J.Zhang, Hongwei2025-04-222025-04-222025-05-01Rashvand M, Ren Y, Sun DW, et al., (2025) Artificial intelligence for prediction of shelf-life of various food products: recent advances and ongoing challenges. Trends in Food Science & Technology, Volume 159, May 2025, Article number 1049890924-2244https://doi.org/10.1016/j.tifs.2025.104989https://dspace.lib.cranfield.ac.uk/handle/1826/23821Background: Accurate estimation of shelf-life is essential to maintain food safety, reduce wastage, and improve supply chain efficiency. Traditional methods such as microbial and chemical analysis, and sensory evaluation provide reproducible results but require time and labor and may not be suitable for real-time or high-throughput applications. The integration of artificial intelligence (AI) with advanced analysis techniques offers a suitable alternative for rapid, data-driven estimation of shelf-life in dynamic storage environments. Approach and scope: The current review assesses the application of AI-based techniques such as machine learning (ML), deep learning (DL), and hybrid approaches in food product shelf life prediction. This study highlights how AI can be utilized to examine data from non-destructive testing methods like hyperspectral imaging, spectroscopy, machine vision, and electronic sensors to enhance predictive performance. The review also describes how AI-based techniques contribute to managing food quality, reduce economic losses, and enhance sustainability by ensuring optimized food distribution and reducing waste. Key findings and conclusions: AI techniques overcome conventional techniques by considering intricate, multi-sourced information capturing microbiological, biochemical, and environmental factors influencing food spoilage. Meat, dairy, fruits and vegetables, and beverage case studies illustrate AI techniques' superiority in real-time monitoring and quality assessment. It also identifies limitations such as data availability, model generalizability, and computational cost, constraining extensive applications. Cloud and Internet of Things (IoT) platform integration into future applications has to be considered to enable real-time decision-making and adaptive modeling. AI can be a paradigm-changing tool in food industries with intelligent, scalable, and low-cost interventions in food safety, waste reduction, and sustainability.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Artificial intelligenceMachine learningFood qualityFood productsDigitalizationIntelligent sensors30 Agricultural, Veterinary and Food Sciences3006 Food SciencesNetworking and Information Technology R&D (NITRD)Data ScienceMachine Learning and Artificial IntelligenceGeneric health relevance12 Responsible Consumption and Production2 Zero HungerFood Science3006 Food sciences4004 Chemical engineeringArticle1879-3053672761104989159