Artificial Intelligence (AI) has emerged as a transformative force in educational evaluation, offering innovative solutions for assessment, feedback generation, performance prediction, learning analytics, and personalized learning. Traditional evaluation methods often face challenges related to scalability, objectivity, timeliness, and individualized feedback. AI-powered educational evaluation systems provide opportunities to automate assessment processes, analyze large datasets, identify learning patterns, and support evidence-based educational decision-making. This study investigates the applications, benefits, challenges, and future implications of Artificial Intelligence in educational evaluation. A mixed-method research design involving 850 students, 250 educators, and 80 educational administrators was employed. Quantitative survey data and qualitative interviews were analyzed to assess perceptions, effectiveness, and ethical concerns associated with AI-driven evaluation systems. Findings indicate that AI significantly enhances assessment efficiency, feedback quality, predictive accuracy, and learning personalization. However, concerns regarding algorithmic bias, data privacy, transparency, and ethical governance remain critical. The study proposes a comprehensive framework for responsible AI implementation in educational evaluation and offers recommendations for institutions seeking to integrate AI-driven assessment systems.