Modern artificial intelligence methods increasingly rely on mathematical frameworks that transform complex molecular and material data into computationally manageable representations. Byencoding chemical compounds, biological entities, and material structures into vector, geometric, and probabilistic spaces, these approaches enable efficient model training, predictive analysis,and generative design. This integration supports applications in drug discovery and materialsscience, where capturing spatial arrangements, symmetry invariances, and uncertainty quantification is essential. Advanced techniques such as graph neural networks, equivariant architectures,and topological data analysis contribute to encoding multi-scale structural and functional information. Multimodal data integration combines chemical, biological, and phenotypic inputs toimprove prediction accuracy and interpretability. Challenges arise in constructing shared representation spaces that accommodate domain-specific features while enabling cross-domain transferlearning. Ethical considerations emphasize transparency, interpretability, and risk mitigation inAI-driven pipelines. The synthesis of algebraic, geometric, probabilistic, and topological methodswithin AI frameworks offers a comprehensive foundation for accelerating discovery and innovationin molecular and material sciences.