The complexity and length of legal documents make it challenging to quickly identify key information. This study examines how natural language processing (NLP) techniques can be used to automate the summarization of legal texts. We implemented six advanced summarization models – Legal Pegasus, BART, Legal LED, Law2Vec, LSA, and T5 – and evaluated their performance separately. To boost accuracy, we created four ensemble models by merging these techniques and assessed their effectiveness. The results reveal the promise of NLP-driven summarization in streamlining legal document analysis and provide a comparative look at the strengths of individual versus ensemble approaches.