The introduction of Automated Essay Scoring systems brought better assessment methods into education through standardized scoring systems that operate at scale while being time efficient. The current AES models function exclusively with English content while neglecting multilingual evaluation, particularly in the Hindi and Marathi languages. A multilingual AES framework has been developed using transformer models XLM-RoBERTa, MuRIL, DistilBERT, and mBERT for conducting context-based essay assessments throughout English, Hindi, and Marathi texts. Through multilingual embeddings combined with fine-tuned models, the system maintains cohesive and coherent, and argumentative quality in essays. The assessment by QWK and RMSE metrics demonstrates both high accuracy and reliability of the system. The highest performance emerged from XLM-RoBERTa and Google MuRIL at 0.78 QWK and 0.77 QWK, respectively.