Social media has become a fundamental device for psychological health from the point of view of general assessment. Research on feelings communicated in a few languages provides an extraordinary comprehension of mental health patterns and diverse psychological examples. This study presents an expanded system for multilingual sentiment examination, particularly for psychological well-being studies. The proposed model uses a trans-previous-based engineering method to oversee etymological assortment and well-being nuances across a few languages by utilizing strong natural language processing (NLP) approaches. Domain-specific datasets assisted with fine-tuning the model, so it would really catch the psychological articulations appropriate to mental health. Utilizing a huge, multilingual dataset taken from social media, the system was surveyed for extraordinary exactness in spotting psychological feelings, such as nervousness, gloom, and trust. The discoveries show the model’s flexibility in cross-lingual settings, as well as its capacity to track down drifts that regular monolingual strategies could miss. In addition, this study explores the results of sentiment examination in brain research, focusing on its utilization in spotting designs, directing medicines, and consequently advancing superior information on mental health on an overall premise. These outcomes feature the potential outcomes of consolidating psychological research with PC strategies to address critical mental health issues.