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Selecting an appropriate career path continues to be a significant concern for students, especially in regions where access to personalized academic counseling is limited. This paper presents Career Craft, an intelligent Education Recommendation System (ERS) designed to apply machine learning techniques to provide personalized career suggestions based on the academic achievements and personal interests of the individual. The framework employs a range of supervised learning algorithms namely XGBoost, Random Forest, and Support Vector Machine (SVM) to enhance predictive reliability. Experimental evaluation on a curated dataset indicates that the Random Forest algorithm yields the best predictive accuracy of 89.1%, surpassing SVM <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(64.71 {\%})$</tex> and XGBoost <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(82.73 {\%})$</tex>. Furthermore, the system integrates multilingual functionality, supporting both English and regional Indian languages such as Hindi, Kannada, and Telugu, thereby extending accessibility to a broader user base. The model has been developed as a web application providing an interactive interface for immediate career suggestions, and it has been developed using Flask. By applying advanced techniques in machine learning and integrating the multilingual accessibility feature, the model proposes a new and extended framework for career counselling in the educational sector.