Access to timely and accurate healthcare diagnosis remains a major challenge in many parts of Nigeria, particularly in rural and underserved communities. The shortage of medical professionals, inadequate triage infrastructure, and long travel distances to healthcare centers often lead to delays in diagnosis and inappropriate self-medication, contributing significantly to preventable morbidity and mortality. This study aimed to address these issues by developing an integrated system that combines an Electronic Health Advisor (EHA) with a rule-based expert system for real-time, offline-capable symptom interpretation and disease classification. The methodology adopted was the Structured Systems Analysis and Design Methodology (SSADM), which guided the systematic analysis, logical specification, and implementation of the system. The tool was implemented using PHP for logic handling, MySQL for database management, and a simple HTML-based interface for user interaction. A simulated dataset consisting of 100 typical rural health scenarios was used to test the system’s diagnostic capability. The system achieved a high agreement with clinical judgment, accurately distinguishing between treatable and severe cases while maintaining high usability among non-technical users. This demonstrates the viability of a lightweight, rule-based diagnostic assistant tailored for deployment in resource-limited settings.