The prevalence of Ear, Nose, and Throat (ENT) diseases presents diagnostic challenges, especially in resource-limited settings. At THB Bekasi Hospital, constrained specialist availability and long consultation queues highlight the need for an accessible diagnostic solution. This study aims to develop an expert system for diagnosing ENT diseases using the Extreme Programming (XP) methodology, incorporating the forward chaining technique for inference. The research includes assessment, knowledge acquisition through expert consultations, system design, and rigorous testing. The system was developed as a mobile application using Android Studio, enabling users to input symptoms and receive real-time diagnostic insights. The knowledge base integrates data from medical experts, synthesizing 11 diseases and 35 symptoms into a robust decision-making framework. The diagnostic process applies predefined rules to ensure accuracy in identifying conditions such as sinusitis, laryngitis, and otitis. Evaluation results demonstrate a 100% match accuracy during testing with 15 test cases, confirming the system’s reliability. The application offers users rapid diagnostic assistance, promoting timely treatment for ENT issues, although it does not substitute medical professionals. By leveraging ubiquitous smartphone access, this system addresses gaps in healthcare accessibility and enhances patient autonomy. This research contributes a scalable framework for deploying expert systems in other medical domains. Future improvements include integrating geolocation services for nearby specialist referrals and adopting backward chaining for more complex diagnoses, thereby broadening its applicability and utility