<p>The Indian Railway system is the fourth largest rail network in the world by size, carrying approximately 24 million passengers daily. In such a huge, dynamic, real-time system, knowing the inconveniences of individuals and optimally taking actions is beyond the scope of the human-operable system. For this, an automated intelligent system capable of recognizing human sentiment from natural language is required. This challenge is heightened because India is a multilingual country. Though some previous studies tried to address this problem by considering Hindi and English, to the best of our knowledge, none have considered Bengali. Bengali is the second most popular language in India, with around 90 million native speakers, and neglecting the mood of the Bengali people will make any system incomplete. To address this gap, a bilingual, automated, natural-language-based mood-detection model was proposed that incorporated Bengali (written in Bengali font), English (written in Latin font), and Bengali (written in Latin font). We created a custom dataset for this purpose by collecting feedback from multiple social platforms. Sentiment detection was achieved through the assignment of weighted values to multilingual words and emojis, and then the model was trained. Internally, various classification algorithms were used to evaluate the effectiveness of the proposed system. Specifically, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Decision Tree classifiers were employed for evaluation. Upon applying these algorithms, performance metrics such as accuracy, precision, recall, F1-score, and cross-validation score were calculated to measure their effectiveness comprehensively. The proposed model achieved a 93% success rate, surpassing the existing models. This research fills a critical gap in multilingual sentiment analysis and lays the foundation in real-time mood detection across large datasets, public feedback systems like Indian Railways.</p>