Abstract: Since teaching is the foundation of education, program accreditation and institutional performance are directly correlated with its effectiveness. By creating a competitive and supportive learning environment, faculty performance has a direct impact on an academic institution’s ability to fulfill its vision and goal. To provide a thorough and impartial assessment of teaching performance, this study uses data mining algorithms to extract insightful information about the elements that go into good instruction, including both structured and unstructured data. This is done in response to the urgent need for faculty performance evaluation. To help institutions identify their strengths, rectify their flaws, and encourage ongoing growth in their teaching and learning processes, the system was created. Looking for trends in teacher data. Furthermore, sentiment analysis methods are employed to assess qualitative input, and Laravel 8.0 provides the framework for putting these algorithms into practice. A grand mean score of 4.38, which is considered “Very Acceptable,” was obtained from expert evaluations of the system, demonstrating its dependability and efficacy in assisting with faculty performance reviews.