Multimodal Large Language Models (MLLMs) are advanced analytics tools that process data across various modalities such as text, audio, image, and video, offering a richer contextual understanding compared to text-only models. These models open up new applications in content creation, personalized recommendations, and human-machine interaction by integrating information from different modalities. Notable MLLMs include Microsoft's Kosmos-1, DeepMind's Flamingo, and Google's PaLM-E, which showcase capabilities in visual dialogue, image captioning, and robotic planning. Despite their potential, MLLMs face challenges such as data alignment, inherited biases, and robustness issues. They operate through a structure involving distinct input, fusion, and output modules tailored to specific tasks. Furthermore, the development of MLLMs is still evolving, with ongoing research addressing their limitations and exploring future directions in multimodal learning.