The application of artificial intelligence (AI) in bioinformatics has shown promising advancements in genomics and proteomics. This study evaluates the performance of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and support vector machines (SVMs) in gene prediction, protein structure prediction, and functional annotation tasks. Our CNN model achieved a remarkable accuracy of 98.5% in predicting gene regions, significantly outperforming the traditional hidden Markov model (HMM) with an accuracy of 91.2%. For protein structure prediction, the RNN model attained a Q3 accuracy of 85.7%, surpassing the 78.4% accuracy of homology modeling methods. The SVM model used for functional annotation of proteins achieved an F1 score of 0.76, compared to 0.68 for a nearest-neighbor approach. These results underscore the superior performance of AI models in bioinformatics, highlighting their potential to revolutionize genomic and proteomic research. Future work should focus on integrating multi-omics data, improving model interpretability, and enhancing computational efficiency. This study demonstrates that AI can significantly enhance the accuracy and efficiency of bioinformatics analyses, paving the way for new insights and applications in the field.