Deep learning in bioinformatics is an evolving research area that leverages neural networks to analyze and interpret complex biological data, enabling breakthroughs in genomics, proteomics, and systems biology. Early applications focused on sequence analysis using convolutional and recurrent neural networks (CNNs and RNNs) for tasks such as DNA/RNA motif discovery, protein secondary structure prediction, and functional annotation. Recent research extends to graph neural networks (GNNs) for modeling molecular and protein–protein interaction networks, variational autoencoders and generative models for designing novel molecules and drugs, and attention-based and transformer architectures for capturing long-range dependencies in biological sequences. Applications include gene expression analysis, drug-target interaction prediction, protein structure modeling, disease diagnosis, and personalized medicine. Current studies also explore multi-omics data integration, transfer learning for low-resource datasets, and explainable AI techniques to ensure biological interpretability, establishing deep learning as a transformative approach for solving complex bioinformatics problems.