MOTIVATION: Network analysis has become a central strategy for dissecting complex biological and environmental systems, particularly as modern omics technologies generate increasingly large and heterogeneous datasets. However, current tools often lack the scalability, flexibility, and native multi-omics support required for high-dimensional data analysis. We developed MetaNet, a high-performance R package that unifies network construction, visualization, and analysis across diverse omics layers. RESULTS: MetaNet enables fast and scalable correlation-based network construction for datasets with more than 10 000 features, providing over 40 layout algorithms, rich annotation utilities, and visualization options compatible with both static and interactive platforms. It further offers comprehensive topological and stability metrics for in-depth network characterization. Benchmarking shows that MetaNet delivers up to a 100-fold improvement in computation time and a 50-fold reduction in memory usage compared to existing R packages. We demonstrate its utility through two representative applications: (1) longitudinal microbial co-occurrence networks revealing airborne microbiome dynamics, and (2) an integrative exposome-transcriptome network of over 40 000 features, uncovering distinct regulatory impacts of biological and chemical exposures. By offering a robust, reproducible, and biologically informed framework, MetaNet advances multi-omics network analysis across biological, ecological, and environmental domains. AVAILABILITY: MetaNet package is freely available at https://github.com/Asa12138/MetaNet.