<p>The convergence of artificial intelligence (AI) and multi-omics is redefining plant science. It moves plant biology from descriptive to predictive and systems-level understanding. Multi-omics frameworks reveal molecular networks driving plant growth, stress adaptation, and crop resilience. However, vast heterogeneity, high dimensionality, and incomplete datasets pose challenges for integration and interpretation. Here, AI and machine learning (ML) are transformative catalysts. They bridge these gaps through modeling, feature extraction, and multimodal learning. This review examines advances in AI-enabled multi-omics integration related to plant enhancement. It covers methodologies from traditional statistical models to deep architectures, including convolutional and recurrent neural networks, graph neural networks, autoencoders, and generative adversarial models. By detailing their roles in dimensionality reduction, missing-value imputation, and network-level prediction, we highlight how AI enhances interpretability, scalability, and cross-species transferability in crop research. Finally, the paper outlines emerging techniques, including single-cell, spatial, and explainable AI frameworks. It emphasizes the need for standardized, accessible, and interpretable pipelines to democratize the use of multi-omics data. Integrating AI and multi-omics promises resilient, high-yielding crop systems that address the sustainability challenges.</p> Graphical abstract <p></p>