Calcium ions (Ca²⁺), as crucial second messengers in plant cells, play a central role in growth, development, metabolic regulation, and stress responses. Systematic analysis of calcium-related genes and signaling pathways is fundamental to understanding calcium regulatory mechanisms and enhancing crop stress resistance. In recent years, with the rapid accumulation of multi-omics data—including genomics, transcriptomics, proteomics, and metabolomics—the application of artificial intelligence (AI) in plant molecular network analysis has advanced significantly, providing new insights for identifying complex signaling pathways and optimizing metabolic networks. This review summarizes recent progress in AI-based identification of calcium-related genes and modeling of signaling pathways, with a particular focus on the application of algorithms such as graph neural networks, convolutional neural networks, and random forests in constructing calcium signaling networks and mining key gene modules. Moreover, it discusses the potential of AI in reconstructing calcium-regulated metabolic dynamics, elucidating stress-responsive signaling patterns, and improving calcium use efficiency and metabolic homeostasis in crops. Finally, the challenges of data integration, algorithm interpretability, and cross-scale modeling are analyzed, and the future directions of AI-driven calcium signaling research in precision agriculture and intelligent breeding are outlined. This review aims to provide a systematic theoretical reference and technical framework for plant calcium signaling studies and crop metabolic optimization.