Emerging Trends in Food Microbiology: A Review of Nanotechnology, Predictive Modeling, and Risk Assessment

ABSTRACT Foodborne diseases remain one of the significant global public health and economic issues, especially in low and middle‐income nations, with the traditional surveillance method of microbiology limited to timely identification and management of foodborne risks. The use of traditional culture‐based processes, though regarded as the gold standard, is time‐consuming and in most cases may fail in the detection of viable‐but‐noncultural microorganisms thus lowering the effectiveness in responding to outbreaks and risk management. Here, modern technologies are changing food microbiology into a predictive and risk‐based science as opposed to a reactive science. The key commonality between nanotechnology, predictive microbiology, and microbial risk assessment is discussed critically in this review with emphasis on these systems in food safety systems in modern times. Nanotechnology solutions such as antimicrobial nanocomposites, smart packaging and nano‐sensors have shown improved sensitivity, specificity and real‐time detection as well as have also helped in improving food preservation and shelf life. At the same time, predictive microbiology has developed over the years due to the combination of kinetic models, machine learning, and artificial intelligence, allowing for proper prediction of microbial behavior under dynamic conditions of processing and storage. These predictive results supply the fundamental quantitative data to microbial risk assessment systems, such as quantitative microbial risk assessment (QMRA), thus facilitating science‐based decisions in industrial and regulatory practices. The review also explains the improvements in genomic surveillance, Bayesian modeling, and integrated One Health, which enhance the strength of hazard identification, exposure assessment, and risk characterization. Although these are encouraging changes, there have been problems in terms of nanoparticle safety, model transferability in food matrices, and ambiguity in exposure dose relations. In general, this review offers a detailed overview of interdisciplinary innovation, highlighting the opportunities that it has in creating proactive, data‐driven, and sustainable food safety management systems.