Multimodal models are increasingly being used across industries due to advancements in language and vision model capabilities. However, Large Language Models (LLMs) cannot understand visual information and Large Vision Models (LVMs) struggle with reasoning tasks. To address this, Multimodal Large Language Models (MLLMs) have been developed, combining the strengths of both LLMs and LVMs to handle multimodal information effectively. Despite their advanced capabilities, MLLMs are prone to hallucination, a phenomenon where they generate content that is not present or accurate based on the input data. Researchers have been actively investigating methods to detect and mitigate hallucinations in Multimodal Large Language Models (MLLMs) and Large Vision-Language Models (LVLMs). These models are driving innovation across various industries by facilitating the interpretation and generation of various modalities, such as text, images, audio, and video. To enhance the accuracy and reliability of these models, ongoing research and innovative approaches are essential.