ARTIFICIAL INTELLIGENCE IN PUBLIC ADMINISTRATION: A DISRUPTIVE FORCE FOR EFFICIENT E-GOVERNANCE

Abstract

Abstract As governments worldwide embrace digital transformation, the need for effective e-Governance systems that optimize public service delivery becomes ever more critical. This paper presents an innovative machine learning-based framework designed to analyze and predict key performance parameters crucial for the enhancement of e-Governance systems. The system focuses on seven essential metrics: Process Efficiency, Citizen Engagement, Cost Savings, Policy Analysis, Transparency, Service Quality, and Infrastructure Optimization. By utilizing three powerful machine learning algorithms—Linear Regression, Random Forest, and Support Vector Machines (SVM)—the framework forecasts trends, improvement rates, and the future trajectory of these parameters over multiple years (2020-2025). The system is built on simulated historical data, reflecting real-world complexities and uncertainties. It allows policymakers to gain actionable insights into performance, empowering them to make data-driven decisions that improve system efficiency and resource allocation. These predictions are visualized in an intuitive manner, enabling stakeholders to compare actual versus forecasted performance, and identify gaps in service delivery that need addressing. Key policy recommendations are automatically generated based on the predictive analysis, such as adopting AI-powered chatbots for enhancing Citizen Engagement, deploying workflow automation tools to improve Process Efficiency, and leveraging predictive maintenance technologies to optimize Infrastructure.