This paper proposes a method for identifying risks in the Magnetic Particle Inspection (MPI) of ferromagnetic material parts (FMP). Based on the Analytic Hierarchy Process (AHP) and Bayesian Belief Network (BBN) combination of probability and the impact, identifying risks that needed to be addressed to improve the quality management system and ensure organizational sustainability. The inspection of critical FMP in the manufacturing and services industry is critical. The selection and use of an adequate analysis method to ensure inspection process reliability is important and can avoid part failure and costly accidents. As a methodological approach, the risk probabilities for the risk factors are loaded into BBN software to assess the probability of occurrence of undesirable events, and AHP is utilized to rank the relative importance of risks. The combination of probabilities and the effects identified the most significant risks. This is the first time this method is being used in this process and the novelty is the combination of BBN with AHP to select significant risks and the use of the Goal Tree dashboard to improve the quality and sustainability of optimization in the inspection of critical parts. The application of the method revealed that significant risks in the inspection of critical hardware are related to operator failure, unfavorable controls and negative organizational factors. The paper proposes responses to risks aiming at preventing the occurrence of failure in the MPI and contributes to the non-destructive inspection (NDI) of the critical parts field.  Has also proposed practical implications and is an invaluable source for (NDI) professionals, safety engineers, and quality managers. The identification and prioritization of risk factors make it easier to allocate resources to prevent critical parts failure and improve product quality and ensure organizational sustainability.