In recent years, the growing usage of social networking sites among youngsters has generated a wealth of data for understanding mental health issues such as depression. The research focuses on the early identification of depression in youth by analysing social media posts in English, Assamese and Hindi Languages. In this study, a dataset of 114,000 posts/comments with 38,000 in each language is utilised to train and test transformer-based models. There are two transformer-based models, BERT and DistilBERT, used for Hybrid deep learning models to compare their effectiveness in analysing for each language. The experimental results show that BERT-based frameworks are effective to early depression identification among youths from multilingual social media data by successfully capturing the contextual characteristics of three languages.