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Simple Summary: As global pet ownership rises, managing the health of aging dogs has become a growing concern, particularly due to the high costs and challenges in recognizing early symptoms. In Korea, many casual pet owners struggle to identify abnormal behaviors and address them effectively without expert knowledge. Thus, we propose a pre-diagnosis system for casual dog owners that infers possible diseases from queries based on observed abnormal symptoms. This proposed system, based on a fuzzy inference algorithm, is not to aiming to compete with veterinarians but to alert the casual pet caregiver to monitor abnormal behavior indicative of disease, to be addressed as early as possible. The experimental results verify that the proposed system is sufficiently robust under noisy input, which is very common when non-expert casual owners use this system. While the pet market is continuously rapidly increasing in Korea, pet dog owners feel uncomfortable in coping with pet dog's health problems in time. In this paper, we propose a pre-diagnosis system based on neuro-fuzzy learning, enabling non-expert users to monitor their pets' health by inputting observed symptoms. To develop such a system, we form a disease–symptom database based on several textbooks with veterinarians' guidance and filtering. The system offers likely disease predictions and recommended coping strategies based on fuzzy inference. We evaluated three fuzzy inference algorithms—PFCM-R, FHAL, and MNFL. While PFCM-R achieved high accuracy with clean data, it struggled with noisy inputs. FHAL showed better noise tolerance but lower precision. PFCM-R is a variant of well-known fuzzy unsupervised learner FCM, and FHAL is the hybrid fuzzy inference engine based on Fuzzy Association Memory and a double-layered FCM we developed. To make the system more robust, we propose the multi-layered neuro-fuzzy learner (MNFL) in this paper, which effectively weakens the association strength between the disease and the observed symptoms, less related to the body part on which the abnormal symptoms are observed. In experiments that are designed to examine how the inference system reacts under increasing noisy input from the user, MNFL achieved 98% accuracy even with non-erroneous inputs, demonstrating superior robustness to other inference engines. This system empowers pet owners to detect health issues early, improving the quality of care and fostering more informed interactions with veterinarians, ultimately enhancing the well-being of companion animals. [ABSTRACT FROM AUTHOR]