The Indian legal system faces significant challenges due to a vast collection of legal documents and a backlog exceeding 40 million cases, impeding efficient judicial process. This paper introduces an advanced automated system leveraging natural language processing (NLP) and machine learning to automate three critical tasks: predicting court judgements with detailed explanations, summarizing extensive legal texts, and providing an interactive legal chatbot. By integrating state-of-the-art models–XLNet and BiGRU for prediction, InLegalBERT for summarization, and Mistral-7b for the chatbot–the system achieves a prediction accuracy of 73.74%, a summarization accuracy of 86.67%, and robust conversational performance. Deployed through a Streamlit interface, it caters to diverse users, from legal professionals needing precise insights to naive individuals seeking accessible guidance. The prediction module offers interpretable outcomes, the summarization tool condenses complex documents into concise formats, and the chatbot delivers tailored legal responses, enhancing research and decision-making efficiency. This unified platform addresses inefficiencies in India’s judicial framework, reducing manual effort and democratizing legal knowledge. Evaluated on real-world Indian legal data, the system sets a benchmark for automation, with potential for future enhancements in multi-lingual support and scalability, promising transformative impacts on legal practice.