This critical assessment methodically examines the integration of artificial intelligence (AI) into higher education grading systems. Conventional grading systems may suffer from subjectivity and inefficiencies, which drives a change toward AI-driven alternatives. The study approach consists of a thorough synthesis and comparative analysis of many peer-reviewed studies aimed at factors like grading accuracy, efficiency, bias reduction, and student satisfaction. Especially in objective assessments like multiple-choice examinations, results indicate that artificial intelligence grading systems greatly improve uniformity and efficiency. Still, marking difficult or subjective assignments presents significant difficulties because artificial intelligence systems sometimes lack the sophisticated judgment of human graders. Furthermore, even if objective criteria help artificial intelligence to reduce biases, further research reveals ethical issues resulting from biases ingrained in AI systems. Although students like the immediacy and impartiality of AI-generated comments, they usually want customized and complex feedback given by human teachers. The study comes to the conclusion that cautious and strategic deployment is required even if artificial intelligence grading systems have several advantages for educational evaluations. Among the recommendations are including adopting artificial intelligence grading as a complementary tool, guaranteeing openness in grading systems, routinely upgrading AI models to reduce bias, supporting teacher development, and keeping human supervision for subjective assessments. We urge more empirical research to continuously enhance AI grading systems and ensure their alignment with learning objectives.