Oak wilt is a destructive fungal disease that affects oak trees, causing rapid defoliation and death if left untreated. Early detection is critical for controlling its spread, minimizing ecosystem damage, and reducing extensive economic consequences. Detection of oak wilt requires expertise as oak wilt symptoms can be confused with other diseases or environmental stressors; to date, the expertise remains limited. This study aims to develop an alternative tool for the early detection of oak wilt disease affecting three oak species, i.e. red oak, white oak, and live oak. The study follows a standard knowledge engineering methodology involving problem assessment, knowledge acquisition, knowledge representation, design, development, and evaluation phases. The domain knowledge was acquired from the secondary knowledge source via knowledge mining activities, analyzed and represented into conceptual maps, flowcharts, rules and inference networks. A total of 26 rules are built in the knowledge base, with 32 primitive premises. The prototype was designed to work in a forward chaining fashion and was developed using JavaScript. The evaluation of the prototype revealed that it successfully detects the presence of oak wilt from the symptoms. The findings also demonstrate the prototype’s usability, especially its ease of use and explanation facility. Early detection will allow timely interventions, reducing oak wilt's ecological and economic impact on forests and communities.