I developed an AI-powered Automated Bubble Sheet & Essay Evaluation System to modernize academic assessment by reducing manual grading effort, improving evaluation consistency, and enabling faster result processing. This system combines Computer Vision, Optical Mark Recognition (OMR), Optical Character Recognition (OCR), Machine Learning, Deep Learning, and Natural Language Processing (NLP) into one end-to-end assessment workflow. It evaluates both objective bubble-sheet responses and descriptive essay answers using a modular, scalable, and cost-efficient architecture. Key Features: • Automated OMR-based bubble sheet grading using image preprocessing, thresholding, fold detection, and answer-mark recognition. • CNN-based classification model for accurate bubble detection, with the best model achieving 98.2% accuracy. • OCR-based text extraction using Google Cloud Vision API for processing scanned/written essay responses. • AI-assisted essay evaluation using GPT-4.1 for rubric generation, semantic analysis, relevance checking, and automated scoring. • Modular architecture with separated OMR, OCR, ML, and NLP layers for better maintainability, scalability, and future enhancement. • Support for structured academic evaluation workflows, including answer key comparison, scoring logic, and result generation.