The current food system is by no means flawless. Despite the advancement in improving the efficiency of food production, the fundamentals of food, which are safety and waste, are still challenges that the entire food industry is trying to overcome. In the U.S., among the food produced, 25% is wasted due to microbial spoilage. The remaining that is consumed, although at low likelihood, is possibly contaminated with pathogens at the level that might cause foodborne disease. The Center of Disease Control and Prevention (CDC) estimates that foodborne disease alone leads to 9.4 million illnesses and 1,351 deaths yearly in the U.S. Novel techniques are urgently needed to continue improving our current food system. Digital tools powered by data-driven or mechanistic models are a promising candidate. While digital technology and modeling tools are increasingly being applied to the food industry, most successful applications are limited to automation of repetitive tasks (e.g., sorting, grading), yield improvement and process control. Unlike the easily measurable outcome of interests in these examples, microbial quantification is challenging as the experimental method, food matrix, and type of microorganism all introduce considerable variability and uncertainty to the measurement. This dissertation aims to provide insight into potential applications and limitations of modeling techniques that help address microbial food safety and spoilage by (i) summarizing and analyzing existing Artificial Intelligence (AI) and Machine Learning (ML) applications in the field of food safety, (ii) developing and deploying two digital tools that can help dairy processors with decision-making in terms of implementation of control strategies to reduce the product spoilage, (iii) suggesting technical approaches that can enhance the data privacy and therefore encourage the food industry to apply these digital tools. The outcome of this dissertation will hopefully shed light on the future of the digital food system in which we can transform our understanding of microbial dynamics into predictive analytics that can, in return, reliably guide us to reduce food safety risks and food waste further.