Abstract: In the modern food industry, ensuring safety and quality is a top priority, with the increasing complexity of food supply chains presenting significant challenges. This paper explores the application of predictive analytics, big data, and machine learning to enhance contaminant detection in food safety management. We review the integration of large-scale data collection from various sources, including sensors, supply chain records, and historical contamination data, to develop predictive models capable of forecasting potential contamination events. By employing advanced machine learning techniques, such as deep learning and ensemble methods, the paper demonstrates how these models can identify patterns and anomalies indicative of contamination risks before they impact consumer safety. Key advantages of predictive analytics include improved detection accuracy, reduced false positives, and timely interventions, which collectively contribute to a more proactive approach in food safety. The paper also discusses the challenges associated with data quality, model interpretability, and the need for continuous model updates to adapt to new types of contaminants. Through case studies and comparative analyses, the research highlights the effectiveness of various predictive analytics frameworks in real-world scenarios, offering practical insights for stakeholders in the food industry. This study underscores the transformative potential of integrating big data and machine learning in safeguarding food quality and public health.