Career planning plays a vital role in shaping a student’s future, yet many students struggle to keep track of their achievements and determine suitable career paths. This paper presents a web-based system designed to help students and faculty systematically record and analyze accomplishments such as certifications, projects, and extracurricular activities. By organizing this data, the system provides students with insights into their progress and suggests potential career opportunities. The system uses machine learning algorithms to analyze student achievements and generate personalized career recommendations. Optical Character Recognition (OCR) extracts relevant details from uploaded documents, while Natural Language Processing (NLP) categorizes and structures the information for better analysis. The platform applies decision trees, random forest, and neural networks to predict career paths, continuously improving accuracy through user feedback. This research contributes to career guidance by offering real-time achievement tracking, an adaptive career recommendation engine, and an evolving dataset that refines predictions as more data is added. The platform empowers students to make informed career decisions by bridging the gap between their academic progress and future professional aspirations.