International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The Indian judiciary is presently facing an unprecedented and worrisome backlog of over 50 million cases that greatly compromises the fundamental constitutional directive of delivering timely justice. While there do exist prevailing digital efforts focused on enhancing judicial system efficiency that stand out, like the pioneering e-Courts Project and National Judicial Data Grid (NJDG), these initiatives to date remain restricted in their impact. To reduce these shortcomings, this document proposes a holistic plan for a single, three-tier AI–Cloud framework that has specifically been developed to efficiently tackle these systemic inefficiencies that hinder the present judicial system. The pivot feature of this work goes in conceptualizing the New India Model (NIM). that deals with laying down outcomes and optimizing procedural efficiency. A simulation over a period of six months, with synthetic and sanitized datasets extracted from sample inputs of datasets from the NJDG, showed a stunning 30% decrease in time taken to clear cases from the backlog with automated implementation of triage procedures. In addition to that, it was found that our Community Operational Performance Model (COPM) obtained a remarkable F1-score of 0.88, that acts to confirm system predictive reliability. During evaluation for a period, our system enjoyed a remarkable availability rate of over 99.9% when run on a hybrid infrastructure of clouds, besides yielding outputs that are explainable with supporting SHAP-based rationales, thereby greatly contributing to end user faith in system trustworthiness overall. Through this proposed study use to utilize cloud-native scalability with explainable AI. Like other previous works focused only on digitization, this proposed framework signifies how AI and Cloud together can virtualize case-related data, Reduce- pendency, and enhance citizen trust in the judiciary mechanism.

Keywords: Artificial Intelligence, Machine Learning, Large Language Models, Random Forest, Convolutional Neural Network (CNN), Intelligent Triage Assistant (ITA), Graph neural networks (GNNs).

Downloads: | DOI: 10.17148/IJARCCE.2025.14909