Abstract: The rising prevalence of food allergies poses significant public health challenges, necessitating innovative solutions for effective detection and management. This research paper presents the development of a Decision Support System (DSS) that leverages Artificial Intelligence (AI) techniques to enhance food allergen detection and management. The proposed system utilizes machine learning algorithms to analyze extensive datasets, including ingredient lists and food labels, to accurately identify potential allergens in food products. By providing real-time recommendations, the DSS assists consumers in making informed choices, aids manufacturers in ensuring product safety, and supports regulatory bodies in compliance monitoring. The system's adaptive learning capabilities enable continuous improvement in allergen detection accuracy, addressing the complexities of modern food production. The DSS offers valuable insights for the development of allergen-free products, promoting innovation within the food industry. Despite challenges related to data quality and system scalability, the integration of AI into allergen management represents a significant advancement in food safety. The paper discusses the system's architecture, machine learning techniques, and potential applications, concluding that AI-driven DSS can significantly reduce the risk of allergic reactions and enhance overall public health. Future research should focus on refining the system and exploring additional applications in food safety.