International Journal of Science, Engineering and TechnologyAuthors: Dongare Suhana Saibaba, Professor Bangar Abhishek P., Professor Karad Vandana A.
Abstract: Public administrative grievance portals across developing and developed nations remain severely strained by high filing volumes, multilingual heterogeneity, unstructured text submissions, and manual departmental routing. This paper presents a structured narrative review of 22 verified sources published between 2018 and 2026, including state-of-the-art public sector deployable models, organized into seven themes: administrative grievance dynamics and digital governance barriers; multilingual natural language processing (NLP) and dialect translation; automated department routing and multi-label text classification; sentiment analysis, priority ranking, and urgency detection; Optical Character Recognition (OCR) for hand-written citizen petitions; generative AI and Large Language Model (LLM) decision support; and privacy-preserving edge deployment and ethical AI governance. Large Language Models, Fine-tuned Transformer architectures (mBERT, XLM-RoBERTa, IndicBERT), and hybrid classifier ensembles achieve over 92% precision in routing accuracy and priority score allocation. However, literature evidence is dominated by single-jurisdiction pilots, synthetic datasets, and fair-weather operational conditions, while cross-lingual dialectical shifts, low-resource language processing, systemic bias in priority scoring, and data privacy remain under-reported. The paper contributes a comprehensive thematic synthesis, a comparative breakdown of fourteen representative studies, a proposed conceptual seven-layer governance architecture, and a prioritized research agenda for trustworthy public sector AI integration.
