An Intelligent Digital Complaint Redressal System for Smart Cities Using Rule-Based NLP and Automated Workflow Optimization

Rapid urbanization and the evolution of smart city initiatives have increased the demand for efficient, transparent, and responsive civic grievance handling systems. Traditional complaint management mechanisms employed by urban local bodies often rely on manual workflows, resulting in delayed responses, lack of accountability, and limited tracking capabilities for citizens. These inefficiencies hinder effective governance and reduce public trust in municipal services. This paper proposes a Digital Complaint Redressal System designed to streamline the lifecycle of civic complaints through automation, structured workflows, and intelligent categorization. The system is implemented using the Django web framework with Python as the backend and SQLite as the database, complemented by a responsive frontend built using Bootstrap. A rule-based natural language processing module automatically categorizes complaints into departments such as water supply, sanitation, roads, and electricity, while also assigning priority levels based on keyword analysis. The architecture supports dual user roles: citizens can submit complaints and track progress in real time, while administrators can manage, update, and resolve issues through a centralized dashboard. The system ensures secure authentication, role-based access control, and structured data storage for analytics. Experimental evaluation demonstrates improved processing speed, enhanced transparency, and efficient complaint resolution compared to conventional systems. The proposed solution offers a scalable, cost-effective, and extensible framework suitable for smart city governance.