Deep learning has transformed and enhanced data analysis and interpretation across various applications in bioinformatics and computational biology. By refining transitions, this review aims to provide a more comprehensive understanding of deep learning’s capabilities and challenges in gene-disease classification and protein structure prediction. Also, this review outcomes reveal deep learning’s ability to improve predictive accuracy, feature extraction, and data transformation in genomics and drug discovery. Recurrent Neural Networks (RNNs) excel in analyzing sequential data, while Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) support image and text analysis. RNNs analyze each element in a sequence while maintaining information from previous steps, making them ideal for understanding gene sequences and identifying patterns within genetic data. CNNs have been particularly implemented in identifying cellular structures in medical imaging, while DNNs are effective in interpreting genomic text data and annotations, thereby enhancing insights into biological relationships. Generative Adversarial Networks (GANs) enable data augmentation to address data scarcity. However, training instability and high computational demands remain challenges. GANs expand the chemical space available for exploration, enabling researchers to augment datasets, enhance training, and increase the robustness of predictive models in identifying potential drug candidates. This review emphasizes the potential of deep learning in biomedicine while advocating for real-world case studies and balanced comparisons to enhance its practical impact.