Multimodal representation learning is a rapidly growing research area in machine learning and deep learning that focuses on learning unified representations from multiple heterogeneous data sources, such as text, images, audio, video, and sensor data. Early approaches relied on simple fusion techniques, including early fusion (feature concatenation) and late fusion (decision-level combination), whereas recent research emphasizes joint embedding spaces, cross-modal attention mechanisms, and transformer-based architectures to capture complex inter-modal relationships. Methods such as contrastive learning, canonical correlation analysis (CCA), graph-based models, and generative models are widely explored to align and integrate information across modalities. Applications span vision-language tasks (image captioning, visual question answering), speech-text understanding, multimodal sentiment analysis, medical imaging, autonomous systems, and human–computer interaction. Current research also addresses challenges like missing or noisy modalities, scalability to large datasets, and transfer learning across tasks, establishing multimodal representation learning as a foundational tool for robust and comprehensive understanding in AI systems.