Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Food safety is a foundational element of public health infrastructure and a key stimulator of socioeconomic development everywhere. Sophisticated food chains, coupled with novel contaminants and changing consumer demand, necessitate robust, evidence-based systems to anticipate and manage threats. Predictive modeling has become an indispensable tool in this process, providing early warning systems, high-risk scenario detection, and inspection and intervention strategy optimization. This paper presents a comparative evaluation of two prevalent methodological paradigms, mathematical models and machine learning algorithms, in food safety risk forecasting. Mathematical models draw on assumptions that are domain-specific and mechanistic processes, while machine learning derives adaptive capabilities from big data. On the basis of systematic review and comparative analysis, this paper scrutinizes their theoretical underpinnings, practical applications, and empirical performances in food safety settings. By combining information from regulatory agencies, scholarly sources, and industrial applications, the study contributes to greater insight into the manner in which these modeling approaches are harmonized or integrated for enhanced efficacy in preventing and managing risk.