<![CDATA[Vaginal discharge is a common gynecological symptom that often indicates various diseases, but its diagnosis frequently requires specialized medical examinations. The limited access to healthcare professionals and the lack of awareness can lead to delayed diagnosis and ineffective treatment, potentially resulting in more severe health complications. This research aims to develop an artificial intelligence (AI) system capable of identifying common vaginal discharge diseases, such as Bacterial Vaginosis, Candidiasis, and Trichomoniasis, based on a series of user-provided symptoms. The study utilizes a rule-based expert system approach with the forward chaining method to process symptoms logically and arrive at a probable diagnosis. The system's knowledge base is constructed from established medical literature and expert physician consultations to ensure high accuracy and reliability. The developed system was tested using a set of clinical cases, achieving an accuracy rate of 92.5%, demonstrating its effectiven ss as a preliminary diagnostic tool. This AI-based system is expected to serve as a valuable early screening instrument, helping to increase access to gynecological health information and facilitating timely medical intervention for women, particularly in areas with limited medical resources.]]>
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