Abstract: Machine Learning (ML) plays a vital role in building intelligent systems that automate eligibility assessment, pattern recognition, and personalized service delivery. In public welfare, ML offers scalable solutions to address challenges such as limited awareness, complex eligibility rules, bureaucratic delays, and low digital literacy—particularly in rural and marginalized communities. This paper introduces Scheme Navigator, a progressive web application designed to simplify access to over 150 central and state welfare schemes in India. The system combines rule-based logic with ML algorithms to dynamically match user profiles with relevant schemes, conduct real-time eligibility checks, and provide geolocation-based guidance using GIS tools. Built using Flask, PostgreSQL, Leaflet.js, Flask-Mail, and Twilio, the platform ensures secure authentication, multilingual accessibility, and timely notifications. A mixed-methods evaluation—incorporating surveys, interviews, and system testing—demonstrates enhanced eligibility awareness, usability, and engagement among users. The study highlights the potential of ML-driven platforms to deliver inclusive, efficient, and transparent welfare services.
Keywords: Machine Learning, Welfare Schemes, Eligibility Prediction, Rule-Based Systems, Progressive Web Application, Geolocation Services, Flask, Government Services, Public Welfare, Digital Inclusion, Multilingual Interface, User Engagement
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DOI:
10.17148/IJARCCE.2025.14518
