The increasing digitization of education has generated vast amounts of learner data, creating opportunities to improve educational outcomes through learning analytics. Learning analytics involves the collection, measurement, analysis, and interpretation of educational data to understand and optimize learning processes. Student assessment, a critical component of educational systems, can benefit significantly from learning analytics by enabling personalized feedback, early identification of at-risk learners, evidence-based interventions, and enhanced decision-making. This study investigates the role of learning analytics in student assessment and examines its impact on academic performance, learner engagement, instructional effectiveness, and institutional decision-making. A mixed-method research design involving 680 students, faculty members, assessment specialists, and educational administrators was employed. Quantitative data were analyzed using descriptive statistics and regression analysis, while qualitative insights were gathered through interviews and focus group discussions. Findings indicate that learning analytics significantly improves assessment accuracy, student performance monitoring, personalized learning, and academic support services. The study concludes that integrating learning analytics into assessment systems can enhance educational quality and foster data-informed teaching and learning practices.