<p>Legal argumentation requires sophisticated reasoning, fact analysis, and a subtle appreciation of statutory and case law. The project provides an AI-based framework that uses large language models (LLMs) to aid legal practitioners by creating context-specific counter-arguments, mimicking courtroom-style argumentation, and case outcome simulation. The model is incorporated with a refined GPT-3.5 Turbo model that was trained on expert-curated triplets of case summaries, legal arguments, and counter-arguments from actual court transcripts and legal discussions. To improve factual grounding and minimize hallucinations, the model is supplemented with retrieval-based legal context from statutory law corpora. A new assessment metric—designed with feedback from working attorneys—was proposed to measure the quality of arguments on grounds of persuasiveness, legal appropriateness, and completeness. Quantitative metrics such as cosine similarity and tailored scoring measures demonstrated that AI-generated counter-arguments attained an average similarity of 0.598 with human-written counterparts and were 6.91% more effective at undermining legal claims in terms of final score reduction. Citation metric is used to evaluate the RAG efficiency. The outcome supports the model’s capability to generate coherent, legally sound refutations. Through closely integrating argument generation, evaluation, and a qualitative outcome simulation, this system provides a scalable, explainable method for adversarial legal reasoning. It sets the stage for future applications in legal education, courtroom preparation, and AI-supported decision-making.</p>