The study explores the use of artificial intelligence (AI) in aviation risk and safety management, focusing on fuzzy dynamic systems. The aviation industry faces challenges due to uncertainties and data fuzziness, making traditional risk assessment methods challenging. The aim is to improve decision-making for aircraft safety, including environmental, pilot, and mechanical health. The main goal is to build an enhanced fuzzy inference system (FIS) with an 85% accuracy rate, utilizing MATLAB computational tools and modular programming. The system's reasoning is clarified through visualization techniques, making it interpretable for stakeholders. This thesis contributes to the growing literature on AI application in aviation, highlighting the potential of fuzzy dynamic systems in safety management.