This survey provides a comprehensive overview of multimodal fusion strategies for multimedia analysis. It discusses various fusion methodologies, including feature-level, decision-level, and hybrid fusion, along with their advantages, limitations, and applications. The paper highlights key challenges in multimodal fusion, such as synchronization, correlation between modalities, confidence levels, and optimal modality selection. It also presents existing research on different fusion approaches, including rule-based, classification-based, and estimation-based methods. The survey emphasizes the importance of selecting the appropriate fusion strategy based on the specific analysis task and the characteristics of the modalities involved. It also discusses open issues and future research directions in the field of multimodal fusion for multimedia analysis. The paper concludes by summarizing the key findings and providing a structured analysis of the different fusion methods used in multimedia research.This survey provides a comprehensive overview of multimodal fusion strategies for multimedia analysis. It discusses various fusion methodologies, including feature-level, decision-level, and hybrid fusion, along with their advantages, limitations, and applications. The paper highlights key challenges in multimodal fusion, such as synchronization, correlation between modalities, confidence levels, and optimal modality selection. It also presents existing research on different fusion approaches, including rule-based, classification-based, and estimation-based methods. The survey emphasizes the importance of selecting the appropriate fusion strategy based on the specific analysis task and the characteristics of the modalities involved. It also discusses open issues and future research directions in the field of multimodal fusion for multimedia analysis. The paper concludes by summarizing the key findings and providing a structured analysis of the different fusion methods used in multimedia research.