The world is evolving and growing every day and the need for dairy products are becoming more evident and essential to human. The higher consumption rate of dairy products by people of different ages has attracted investors because of its economic values. Considering this growth and its economic benefits, the understanding of the risk involved in dairy products manufacturing processes is highly required. The objective of this research is to develop an intelligent system capable of analyzing risk level of dairy products manufacturing system at different categories (Physical, Biological, Chemical, and Environmental) of the operation, and the final risk evaluation of the manufacturing system. Five Mamdani Fuzzy (FIS) Inference System models were proposed to solve this problem. FIS has been proven to be a great tool to assess risk at different levels. The first stage of the study involved gathering data to identify the failure modes using data from operation failures, root-cause analysis log, consumer feedbacks, and expert’s opinions. These data were used to define the membership functions for the first four FISs, with the expert’s knowledge and opinions. The output of this first four FISs then fed into the final FIS to evaluate the risk level of the manufacturing system. The proposed novel model uses fuzzy logic, experts’ knowledge and quantitative-based approach on these three criteria (Severity, Occurrence, and Detectability) and linguistic terms (Very_Small, Small, Medium, High, Very_High) to analyze and evaluate the risk involved in dairy products manufacturing. The result of this research work will give both the manufacturers and the consumers guarantees on the finished products but most importantly, it can make the operation managers more productive. Since the failures are prioritized, the maintenance team can schedule maintenance to address the most important failure and can employ the approach of other manufacturers as a benchmark. It is worthy of note that the model gives a deep insight on how to mitigate the risks involved in dairy products manufacturing systems. Models were experimented using data provided by a dairy products manufacturer to validate the model and Graphic User Interfaces were designed as a platform to provide the inputs to the proposed model. Keywords: Fuzzy, Mamdani fuzzy inference system, Linguistic terms, Risk evaluation, Occurrence, Severity, Detectability, Failure Mode and Effects Analysis