Large language models (LLMs) have significantly transformed the interpretation and creation of human language in the field of computerized language processing. These models, based on deep learning techniques such as transformer architectures, are trained on massive text datasets. This study provides an in-depth analysis of LLMs, covering their architecture, historical development, and applications in education, healthcare, and finance. LLMs are capable of providing logical responses by interpreting complex verbal patterns, making them valuable in various real-world scenarios. However, their development and implementation raise ethical concerns and have societal implications. Understanding the importance and limitations of LLMs is crucial for guiding future research and ensuring the ethical use of their potential. This survey highlights the impact of these models as they evolve, providing a roadmap for researchers, developers, and policymakers navigating the world of artificial intelligence and language processing. LLMs are deep learning models trained on large text datasets to understand and generate human-like text. They have gained significant attention due to their ability to understand and generate human language, as exemplified by models like GPT-3 and BERT. LLMs have advanced natural language processing (NLP) by enabling a wide range of tasks, including text abstraction, which involves content extraction and summarization. These models, powered by transformer architectures, have become a powerful tool in the field of AI, with their parameter count often serving as a measure of their complexity and learning ability. The parameter count is crucial in determining the model's capacity to capture context and linguistic subtleties. As LLMs continue to evolve, their impact on various sectors is expected to grow, necessitating careful consideration of their ethical and societal implications.Large language models (LLMs) have significantly transformed the interpretation and creation of human language in the field of computerized language processing. These models, based on deep learning techniques such as transformer architectures, are trained on massive text datasets. This study provides an in-depth analysis of LLMs, covering their architecture, historical development, and applications in education, healthcare, and finance. LLMs are capable of providing logical responses by interpreting complex verbal patterns, making them valuable in various real-world scenarios. However, their development and implementation raise ethical concerns and have societal implications. Understanding the importance and limitations of LLMs is crucial for guiding future research and ensuring the ethical use of their potential. This survey highlights the impact of these models as they evolve, providing a roadmap for researchers, developers, and policymakers navigating the world of artificial intelligence and language processing. LLMs are deep learning models trained on large text datasets to understand and generate human-like text. They have gained significant attention due to their ability to understand and generate human language, as exemplified by models like GPT-3 and BERT. LLMs have advanced natural language processing (NLP) by enabling a wide range of tasks, including text abstraction, which involves content extraction and summarization. These models, powered by transformer architectures, have become a powerful tool in the field of AI, with their parameter count often serving as a measure of their complexity and learning ability. The parameter count is crucial in determining the model's capacity to capture context and linguistic subtleties. As LLMs continue to evolve, their impact on various sectors is expected to grow, necessitating careful consideration of their ethical and societal implications.