The research explores the development of a predictive model that leverages citation and publication metrics to estimate future research impact (RI). The model is designed to assist academic institutions and faculty members in analyzing their scholarly output by using key performance indicators such as the number of publications, citation counts, h-index, and previous impact scores. Data is collected through web scraping from academic databases, and advanced algorithms are applied to generate a predicted impact score (IS). The system’s predictions provide valuable insights for academic career planning, grant applications, and profile enhancement. By focusing on both the quantity and quality of publications, the model accurately reflects a researcher's influence within their field. A comprehensive data visualization component is integrated to allow users to easily interpret the predicted results and track progress over time. This tool offers a significant advantage for researchers by helping them identify areas for improvement and monitor their academic growth. The results indicate a strong correlation between the predicted scores and actual impact measures, validating the effectiveness of the approach. The research also identifies limitations in handling non-traditional scholarly outputs, suggesting potential improvements for future versions of the model. This research aims to provide an efficient and scalable solution for academic institutions seeking to enhance faculty RI, offering personalized recommendations for career development.