Automated assessment systems, such as Automated Essay Scoring (AES), have rapidly gained prominence in education and professional evaluations, driven by the need for scalability, efficiency, and reduced bias. This paper introduces the Autonomous Evaluation and Assessment System (AEAS), leveraging advancements in Artificial Intelligence (AI) and Machine Learning (ML) to evaluate multiple types of assessments, including MCQs, essays, coding submissions, and plagiarism checks. AEAS uses AI algorithms to provide accurate, realtime feedback, capable of handling thousands of students or applicants. Subjective question evaluations are powered by Natural Language Processing (NLP) and machine learning models, offering suggestions for improvement. The system’s modular design allows for scalability and future enhancements. Experiments showed mixed results, but overall, AEAS improved user satisfaction and significantly reduced teacher workload, providing immediate feedback. While it is not yet capable of evaluating creative or context-based assessments, AEAS remains a scalable solution with potential for further operational improvements.