This paper provides an extensive review of multi-modal large language models (MM-LLMs) and their applications. It discusses the evolution of large language models (LLMs), focusing on their development from traditional statistical models to transformer-based architectures like BERT and GPT. The paper highlights the role of attention mechanisms in enhancing model performance and covers the major LLMs and MM-LLMs, including GPT, CLAUDE, Gemini, LLaMA, Mistral, Falcon, and others. It also discusses techniques for model tuning, such as fine-tuning and prompt engineering, and addresses ethical considerations like data bias and model misuse. The paper compares proprietary and open-source models, emphasizing the benefits of open-source models in terms of cost-effectiveness, transparency, and flexibility. It also explores the implications of open-source versus proprietary models in AI research. The review includes a detailed analysis of various MM-LLMs, such as BLIP-2, CLIP, LLaVA, Kosmos-1, MiniGPT4, and mPLUG-OWL, and their capabilities in multi-modal tasks like image captioning, text-to-image generation, and visual question answering. The paper also discusses the challenges and limitations of these models, including hallucinations and computational costs, and provides insights into their potential applications in various domains. The review concludes with a discussion on the future directions of MM-LLMs and their impact on AI research and development.This paper provides an extensive review of multi-modal large language models (MM-LLMs) and their applications. It discusses the evolution of large language models (LLMs), focusing on their development from traditional statistical models to transformer-based architectures like BERT and GPT. The paper highlights the role of attention mechanisms in enhancing model performance and covers the major LLMs and MM-LLMs, including GPT, CLAUDE, Gemini, LLaMA, Mistral, Falcon, and others. It also discusses techniques for model tuning, such as fine-tuning and prompt engineering, and addresses ethical considerations like data bias and model misuse. The paper compares proprietary and open-source models, emphasizing the benefits of open-source models in terms of cost-effectiveness, transparency, and flexibility. It also explores the implications of open-source versus proprietary models in AI research. The review includes a detailed analysis of various MM-LLMs, such as BLIP-2, CLIP, LLaVA, Kosmos-1, MiniGPT4, and mPLUG-OWL, and their capabilities in multi-modal tasks like image captioning, text-to-image generation, and visual question answering. The paper also discusses the challenges and limitations of these models, including hallucinations and computational costs, and provides insights into their potential applications in various domains. The review concludes with a discussion on the future directions of MM-LLMs and their impact on AI research and development.