Biological data have grown exponentially due to single-cell multi-omics, high-content imaging, and large-scale clinical records; this has transformed modern biology. The landscape has shifted to one of data abundance, and the current approaches to computational analysis are not up to the task of dealing with the scale and complexity of this change. The information in big biological data can be characterized by the volume it occupies, velocity, variety, veracity, and value, and each of these features presents its own challenges to the researcher. Artificial intelligence (AI), including machine learning (ML), deep learning (DL), and natural language processing (NLP), has progressively turned into a requirement in tackling these issues. This chapter discusses how AI can be used to curate, represent, learn, and model the analysis of big biological data. Questions include data curation via methods to compress, represent, and outline big biological data, how to learn representations, how to model with big biological data, and how to translate into clinical problems. Using the conversation of genomics, proteomics, transcriptomics, structural biology, imaging, and electronic health records, this chapter reveals how AI is transformative in bioinformatics. In conclusion, the chapter points to enduring challenges, namely interpretability, data governance, and reproducibility, and provides a step into the future of explainable, trustworthy, and reproducible AI in biology.