The persistent accumulation of unresolved legal cases, especially within the Indian judiciary, significantly hampers the timely delivery of justice. Manual methods of prioritizing petitions are often prone to inefficiencies and subjective biases further exacerbating delays. To address this issue, we propose LLMPR (Large Language Model-based Petition Ranking), an automated framework that utilizes transfer learning and machine learning to assign priority rankings to legal petitions based on their contextual urgency. Leveraging the ILDC dataset comprising 7,593 annotated petitions, we process unstructured legal text and extract features through various embedding techniques, including DistilBERT, LegalBERT, and MiniLM. These textual embeddings are combined with quantitative indicators such as gap days, rank scores, and word counts to train multiple machine learning models, including Random Forest, Decision Tree, XGBoost, LightGBM, and CatBoost. Our experiments demonstrate that Random Forest and Decision Tree models yield superior performance, with accuracy exceeding 99% and a Spearman rank correlation of 0.99. Notably, models using only numerical features achieve nearly optimal ranking results (R2 = 0.988, \r{ho} = 0.998), while LLM-based embeddings offer only marginal gains. These findings suggest that automated petition ranking can effectively streamline judicial workflows, reduce case backlog, and improve fairness in legal prioritization.

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In 2017, U.S. state trial courts received a gastronomical 83 million court cases. The Chinese Civil Law system sees over 19 million cases per year, with only 120,000 judges to rule over them. In the OECD area (consisting of most high-income economies), the average length for civil proceedings is 240 days in the first instance; the final disposition of cases often involves a long process of appeals, which in some countries can go up to 7 years. It's no secret that the judiciary system in many countries is long, tedious, slow, and can cause months of misery, pain, and anxiety to individuals, families, corporations, and litigators. Moreover, when cases do see the light of day in court, the outcome is not always satisfactory, with high-profile cases especially receiving criticism for being plagued by judge biases' and personal preferences. Scholarly research suggests that in the United States, judges' personal backgrounds, professional experiences, life experiences, and partisan ideologies might impact their decision-making.

In spite of incidents of inaccuracy and bias, agencies like Artificial Intelligence (AI) court judges are starting to get accepted. However, AI has a lot to learn before we allow it to judge our moral behavior. Ganes Kesari, Co-Founder and Head of Analytics at Gramener, tells The Sociable that right now AI is not ready to take decisions on cases, and even in the future, it would be better off in the court in an assistant's role. AI needs to acquire skills in'understanding' context and interpreting scenarios "Today, AI is more suited to play the role of a judicial assistant than that of a criminal judge. It is smart at processing details, summarizing cases and looking up references. It is not ready to take decisions on cases just as yet," he says.