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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 11 | Nov 2025

p-ISSN: 2395-0072

www.irjet.net

PREDICTIVE ANALYSIS SYSTEM FOR JUDICIAL CASE OUTCOMES USING AI Hemashree H C1, Jeevan M S2, Dhanush S Kabbur3, Smruthi M S4, Vindhya V Alur5 1 Asst Prof. Information Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere,

affiliated to VTU Belagavi, Karnataka, India 2 3 4 5 Bachelor of Engineering, Information Science and Engineering, Bapuji Institute of Engineering and Technology

Davangere, affiliated to VTU Belagavi, Karnataka, India ---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract - This project presents a system that can predict

The following studies provide a strong foundation for the development of predictive systems that analyze legal texts and forecast judicial outcomes.

the outcome of judicial cases through analyzing prior judgments and legal documents. The model identifies relevant patterns, extracts key case features, and predicts results such as judgment type or case duration using machine learning and NLP. The system supports lawyers and researchers with fast insights into improving decision-making efficiency, enabling transparency and reducing manual effort.

SL. NO

AUTHOR/ YEAR

PAPER TITLE

TECHUSED

FINDINGS

Aletr a et.al

Predicting judicial decisions of the European Court of human

NLP+ SVM

Achieved

Key Words- Judicial Case Prediction, Artificial Intelligence

(AI), Machine Learning, Natural Language Processing (NLP)

Sule et al.(2017)

Using Machine learning to Predicted Decisions of the ECtHR

Random Forest

Identified text and meta data as predicating

Legal Text classification

SVM, Na

Demonstrated Feasibility of Predicting

vie Bayes

Table -1: Literature Survey The prediction of judicial decisions using AI and machine learning techniques has been a point of investigation in various studies. Aletra et al. (2016) made an application of NLP and support vector machines on the decisions of the European Court of Human Rights and reported almost 79% accuracy based on text features alone. Later, Medvedeva et al. (2019) developed a model that used Random Forest classifiers and successfully predicted the decisions of the ECtHR; it showed that both textual content and case metadata are significant predictors. Sule et al. (2017) focused on the classification of legal text with the help of SVM and Naïve Bayes and proved that automated prediction models are feasible and have a lot of potential in the legal domain. In the meantime, Dsouza and Anand (2020) proposed a decision tree-based model that specifically predicts Indian judicial data and achieved an accuracy of about 70%. Collectively, these studies demonstrate that machine learning and NLPbased analyses effectively interpret legal documents and predict outcomes within disparate judicial systems.

The project introduces the Predictive Analysis System for Judicial Case Outcomes, which is designed to forecast judgment types and case duration by processing past legal data. This system will abstract necessary features from legal documents and utilize machine learning models in order to make data-driven predictions. While it does not aim at supplanting judicial decision-making, it rather enhances efficiency, transparency, and decision support within the legal domain.

1.1 LITERATURE SURVEY The application of Artificial Intelligence (AI) and Machine Learning (ML) in the legal domain has gained considerable attention over the last decade, especially in the area of judicial decision prediction. Various researchers have explored different techniques, datasets, and approaches to understand and model judicial reasoning computationally.

Impact Factor value: 8.315

Medvede vaet al.(2019)

The judicial system generates a huge volume of legal documents; therefore, manual analysis is slow, complicated, and delayed. With an increasing demand for quicker and more accurate legal insights, technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) are useful. These tools can analyze historical judgments, identify key patterns, and support legal professionals in understanding case trends.

Accuracy using Text- based feature

Rights

1. INTRODUCTION

~79%

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