The growing volume of digital data in different forms—such as text, images, audio, and video—has created a strong demand for advanced summarization techniques that can handle information beyond plain text. Traditional text-based summarization methods often fall short in capturing the complete context when other modalities are present. Multimodal summarization aims to bridge this gap by combining data of different types to generate summaries that are more informative, coherent, and contextually rich. This paper presents a comprehensive survey of the field of multimodal summarization, focusing on major techniques, publicly available datasets, and standard evaluation metrics with respect to both extractive and abstractive. The paper highlights recent developments in the area and organizes existing work based on input types and summarization strategies. In addition, current limitations are identified, and future research directions are given to enhance the accuracy, scalability, and practical applicability of multimodal summarization systems. The survey aims to serve as a valuable resource for researchers and practitioners interested in advancing the capabilities of summarization in today’s multimedia-rich environment.