Abstract—Rural governance in India continues to face systemic inefficiencies rooted in paper- intensive workflows, geographic barriers, and the absence of real-time service visibility. This paper presents DigiGram, a full-stack digital governance platform purpose-built for Kasbe Digraj Gram Panchayat, Sangli district, Maharashtra, serving a population exceeding 15,000+ citizens. The platform consolidates complaint registration, certificate processing, property and water tax management, government scheme discovery, meeting management, and a public-facing AI chatbot into a single web-based system. A distinguishing technical contribution is a multi-classifier ensemble machine learning microservice—combining Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine with TF-IDF (3,000 features, n-grams 1–3) and soft voting—that automatically assigns High, Medium, or Low priority labels to citizen complaints with an achieved accuracy of 78–85%. The system is architected on React.js (frontend), Spring Boot Java 17 (backend REST APIs), Firebase Firestore (real-time NoSQL), Cloudinary and Supabase (media and PDF storage), and a Python Flask inference microservice. Security is enforced through Firebase OTP-based citizen authentication and email-password admin authentication, with role-aware middleware on every API endpoint. Deployment results demonstrate measurable improvements in service throughput, complaint resolution time, and administrative transparency, establishing DigiGram as a replicable model for Panchayat-level digital transformation. Keywords — Digital governance, gram panchayat, machine learning, complaint prioritization, e-governance, rural India, ensemble learning, Firebase, Spring Boot, natural language processing, real-time systems.