The emergence of Large Language Models (LLMs) represents one of the most consequential shifts in the history of artificial intelligence (AI) and natural language processing (NLP). Built on the Transformer architecture with self-attention mechanisms, LLMs such as BERT, GPT-3, T5, LLaMA, and GPT-4 have achieved state-of-the-art performance across a broad spectrum of linguistic tasks, fundamentally reshaping how machines comprehend and generate human language. This survey presents a systematic and comprehensive review of the evolution of NLP—from rule-based and statistical methods to the current era of foundation models—examining key architectural innovations, pre-training objectives, fine-tuning strategies including parameter-efficient methods such as Low-Rank Adaptation (LoRA), and alignment techniques including Reinforcement Learning from Human Feedback (RLHF). We critically assess performance across standard benchmarks including GLUE, SuperGLUE, and MMLU, and analyze persistent challenges such as hallucination, bias, computational cost, and explainability. Furthermore, we explore the expanding landscape of LLM applications in healthcare, education, legal reasoning, and code generation, and outline promising future directions including multimodal models, efficient inference, and AI alignment. This work aims to serve as both an accessible introduction and a scholarly reference for researchers and practitioners engaged with the rapidly evolving frontier of AI-powered language understanding.