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The increasing volume of legal documents and the complicated nature of legal complications pose significant challenges to legal professionals, academics, and clients who need effective document analysis. We present a hybrid NLP and Machine Learning approach to automate legal text processing. This paper introduces a comprehensive approach to an AI-powered legal document analysis tool that can automate legal text processing using an integrated NLP and ML model for summarization, clause extraction, document classification, and question-answering. Through multilingual features, fine-grained clause risk assessment, and jurisdiction specific legal mapping, the system identifies key flaws of established legal document analysis tools. We demonstrate that this hybrid approach makes substantial gains over single-method baselines with 91.37% precision in extractive summarization and 85.24% precision in abstractive summarization. This helps automate legal workflows up to a 60% reduction in manually reviewing data from multiple types of legal sources by increasing the accessibility of legal material across jurisdictions.