Evaluating student performance is crucial in education. The prevalent method relies on human evaluators, but with expanding student teacher ratios, this becomes complex. Manual assessments are susceptible to unintentional biases and inconsistencies. Technology, a valuable tool in education, is replacing traditional pen-and-paper assessments with digital methods. While effective for multiple-choice questions, there’s a rising need for innovative approaches to evaluate descriptive answers due to the variability in student responses. The proposed system is designed to intelligently and efficiently evaluate descriptive responses, whether handwritten or typed. By leveraging Optical Character Recognition (OCR) technologies including OpenCV along with MXNet framework and ResNet34, the system digitizes answer scripts, enabling evaluation against a model answer key. The responses are categorized into specific and conceptual questions, each employing distinct evaluation models, incorporating various similarity measures like cosine similarity, fuzzy string matching and BERT along with natural language processing (NLP) techniques and concept graph generation to ensure accurate grading. Additionally, the system checks for plagiarism within and across answer scripts using techniques like K Means algorithm for intrinsic plagiarism detection and similarity measures for extrinsic plagiarism detection, identifying instances of malpractice. This paper introduces a web application incorporating these features, providing an end-to-end pipeline and facilitating a seamless process for the evaluation and analysis of student answer scripts with descriptive responses. Despite a slight accuracy reduction to 92.18%, iSEAD’s consideration of clean handwriting and swift plagiarism checks showcases its superior effectiveness over manual grading by testing out to be 10 times faster than the later.