During a child's growth and development, inadequate nutrition can impede both physical and intellectual development. Although many people perceive these issues as commonplace, neglecting them can lead to severe consequences. To address the challenge of a limited number of nutritionists and a growing number of patients, this final project introduces an expert system designed to identify malnutrition in toddlers. The expert system conducts a diagnosis of malnutrition based on observed symptoms and offers recommendations for addressing the issues associated with malnutrition in toddlers. This expert system aims to empower parents to independently identify their children's malnutrition types, potentially alleviating the shortage of nutritionists in the healthcare system. The expert in this study is a nutritionist working at Puskesmas Berkilau Pangkalan Kerinci 2. If the knowledge base and production rules, which consist of comprehensive and accurate information, are in place, they can be applied to develop an inference engine. In this phase, the application guides users in inputting facts (characteristics), enabling the generation of conclusions related to toddler nutrition levels. The knowledge stored in the knowledge base and production rules serves as the foundation for the inference engine