The proliferation of Artificial Intelligence (AI) triggers explainable AI (xAI) to be a very promising tool in our everyday life. The black box AI models with less explainability can cause grave issues in legal practices. Therefore, in the field of judiciary, xAI enhances transparency and credibility by overcoming lack of proper reasoning in the black box AI models. This paper aims to present a comprehensive xAI framework for the judicial system to enhance the interpretability using SHAP and LIME. The article encompasses meticulous data collection from diverse judicial sources, followed by robust data preprocessing and feature engineering techniques to ensure high-quality input for the models. Utilizing this data, advanced machine learning algorithms, including both traditional and deep learning models are implemented. Furthermore, the execution steps forward with explainable models including gradient boosting ensemble and explainability techniques. This approach provides clear and insightful explanations for model predictions, enhancing trust and reliability in xAI-driven judicial decisions. The research work not only aims to improve the accuracy of legal predictions but also ensures that the decision-making process is transparent and understandable to legal professionals and stakeholders for the improvement of verdicts in the legal decision-making process.