Multi-modal data refers to datasets that contain multiple types of information or data modalities, such as text, images, audio, video, or sensor data. This field of research focuses on developing methods and algorithms for integrating and analyzing these diverse modalities of data to gain deeper insights and improve decision-making. Multi-modal data analysis has applications in various fields, including machine learning, computer vision, natural language processing, healthcare, and social media analysis. The goal of multi-modal data analysis is to extract complementary information from different modalities to improve the accuracy and robustness of data analysis tasks.