<b>Redefining Research Assessment: AI and Machine Learning Strategies for Assessing Scholarly Impact</b>

Citation counts and journal metrics have historically been used to assess scholarly impact; they often do not capture the nuances of impacts related to the overall academic work. This article considers how ML and AI may significantly reshape research evaluation as more detailed, accurate, and prescient frameworks could dominate that landscape. Significant progress was made on AI-based models for predicting citation, analysis by altmetrics, appraisal of trajectory at an early career, and impact at a journal level. AI and ML provide real-time, dynamic insights on research contributions across disciplines through methods such as natural language processing, network-based metrics, and predictive analytics. This paper also addresses ethical issues related to AI-driven assessment, including algorithmic bias, transparency, and data privacy, with a focus on the necessity of inclusive and equitable metrics. The incorporation of AI and ML in scholarly evaluation encourages creativity, teamwork, and evidence-based decision-making within academia, bringing fresh chances to understand the wider effects of research. These technologies are recommended to be implemented in the work's conclusion so as to give a more complex, fair, and progressive approach to gauge scholarly effect in a world that puts its priority on digitalization.