Railway stations serve linguistically diverse populations requiring fast, accurate dissemination of time-critical information including arrivals, departures, platform changes, and safety advisories. This survey examines multimodal language technologies for smart railway environments, critically analyzing speech recognition, machine translation, and accessibility systems. Through systematic review of domain-relevant studies, we identify principal technical challenges: acoustic degradation in noisy reverberant environments, translation quality for low- resource Indian languages, strict latency requirements for mission-critical announcements, preservation of critical slot values (train identifiers, platform numbers, times), and multimodal accessibility needs. The paper provides comparative analysis of existing approaches across automatic speech recognition, neural machine translation, text-to-speech synthesis, and sign-language rendering, revealing gaps in current solutions particularly for high- noise conditions and low-resource languages. We propose a hybrid architecture combining verified low-latency on- device processing for time-critical announcements with cloud-assisted domain-adapted models for enriched interactions. Key contributions include a comparative taxonomy of deployment patterns, critical analysis of evaluation practices beyond corpus-level metrics, identification of recurring failure modes in field deployments, and a practical system blueprint emphasizing slot-preservation verification, privacy-aware data handling, and human-in-the-loop safeguards. This work bridges research prototypes and operational deployment, providing foundations for accessible, reliable multilingual information systems in railway environments.