| With the acceleration of global population aging, osteoporosis has emerged as a significant chronic disease that poses a substantial threat to human health. The complexity of its pathological mechanisms, coupled with the inadequacies in precise diagnosis and treatment, presents formidable challenges for public health systems. Bioinformatics provides innovative tools for systematically elucidating the molecular regulatory networks of osteoporosis, thus facilitating the advancement of precision medicine. This paper systematically reviews the progress of bioinformatics techniques, including genomics, transcriptomics, proteomics, and machine learning, in osteoporosis research. Specifically, genomics has revealed genetic susceptibilities associated with key loci and pathways. Transcriptomics has clarified the cellular heterogeneity and regulatory mechanisms underlying the dynamic balance of bone metabolism. Proteomics has identified potential therapeutic targets. Machine learning has enabled risk prediction and imaging-assisted analysis through the modeling of multidimensional data. Furthermore, the study addresses current challenges, such as the heterogeneity of multi-omics data, delays in causal validation, and bottlenecks in clinical translation. Future efforts should focus on integrating multi-model AI models to synthesize "gene-environment-phenotype" cross-scale networks, validating causal effects with large-scale cohort studies, and promoting the application of real-world evidence. These strategies are essential for transitioning osteoporosis research from empirical treatment to precision intervention, ultimately ensuring skeletal health in an aging society. |