Multimodal Large Language Models (MLLMs) are rapidly changing the AI landscape, blending text, images, audio, and video to achieve a deeper understanding of the world. But how do these complex systems actually work, and can we trust their decisions? This is where explainable and interpretable AI (XAI) comes in. This field aims to shed light on the inner workings of these “black box” models, making their processes more transparent and trustworthy. Recent research proposes a novel framework for categorizing XAI methods for MLLMs, examining them through three key lenses: the data used, the model's architecture, and the training and inference processes. By understanding how data is attributed to the model’s outputs, we can gain insights into its reasoning. Analyzing the model itself, from individual tokens and embeddings to the interplay between neurons and layers, helps uncover how it represents and processes multimodal information. Finally, examining the training and inference strategies reveals how transparency is affected throughout the MLLM's lifecycle, addressing issues such as alignment, hallucinations (generating incorrect or unsupported information), and overall robustness. This multifaceted approach is crucial for developing more accountable and reliable multimodal AI, paving the way for its use in critical applications like autonomous driving, medical diagnosis, and robotics. While significant progress has been made, further research is needed to develop unified frameworks for evaluating and improving MLLM explainability, particularly as these models become increasingly complex and integrated into our lives. Building datasets that encompass more modalities and real-world scenarios, understanding how different architectural components interact, and developing training strategies that prioritize transparency are vital steps toward unlocking the full potential of MLLMs while ensuring their responsible deployment.