The Indian judicial system faces a critical challenge with approximately 52 million pending cases, causing significant delays that impact socio-economic stability. This study proposes a cloud-based software framework to classify and prioritize court cases using algorithmic methods based on parameters such as severity of crime committed, responsibility of parties involved, case filing dates, previous hearing's data, priority level (e.g., Urgent, Medium, Ordinary) provided as input, and relevant Indian Penal Code (IPC), Code of Criminal Procedure (CrPC), and other legal sections (e.g., Hindu Marriage Act, Indian Contract Act). Cases are initially entered by advocates on record or court registrars, followed by automated hearing date allocation that balances fresh and old cases while accounting for court holidays and leaves. The system streamlines appellate processes by fetching data from historical case databases. Our methodology integrates algorithmic prioritization, a robust notification system, and judicial interaction, with features that allow judges to view daily case counts and their details. Simulations demonstrate that the system can process cases efficiently, with reliable notification delivery and positive user satisfaction among judges and registrars. Future iterations will incorporate advanced machine learning for dynamic prioritization, addressing critical gaps in existing court case management systems to enhance efficiency and reduce backlogs.

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