Cross-modal semantic integration is a process of aligning different data modalities, such as text, images, audio, and video, into unified semantic representations that enable AI systems to understand relationships and meanings across diverse data types. The challenges in cross-modal semantic integration include semantic inconsistencies between modalities, architectural complexity, and data quality issues. To address these challenges, strategies such as dual-encoder architectures, contrastive learning techniques, temperature scaling, and attention-based fusion can be used. These approaches enable the creation of shared understanding where textual descriptions, visual content, and audio signals can be compared, searched, and reasoned about within the same conceptual framework. By implementing proper evaluation frameworks and monitoring infrastructure, cross-modal semantic integration can transform enterprise multimodal AI capabilities.