<p>The post-genomic era has witnessed an unprecedented accumulation of biological data driven by high-throughput technologies such as next-generation sequencing, transcriptomics, microarrays, genotyping-by-sequencing, and proteomics. Although these advances have enabled comprehensive exploration of complex biological systems, the resulting data volume, dimensionality, and heterogeneity pose substantial computational and analytical challenges. This review provides a structured overview of data mining and computational approaches commonly used for biological big data analysis, with a focus on their applications across genomics, transcriptomics, proteomics, and systems biology. Specifically, we summarize widely adopted analytical workflows and bioinformatics tools for NGS, transcriptome, GBS, QTL, and microarray data analysis, and illustrate their utility through representative applications in disease diagnosis, drug discovery, developmental biology, agriculture, and precision medicine. In addition to outlining methodological capabilities, the review discusses key limitations encountered in practice, including validation of experimentation data, data integration across platforms, scalability, and challenges in biological interpretation. By consolidating current methodologies, tools, and application domains, this review aims to provide a practical reference for researchers navigating biological big data analysis while highlighting areas where further methodological development is needed.</p>