Recent advancements in omics technologies have generated vast amounts of complex biological data, providing a deeper molecular understanding of cancer. Leveraging this data for cancer classification and prediction is crucial for early detection, effective treatment, and improved patient outcomes. This study compares various deep learning models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Belief Networks (DBNs), and Autoencoders, using diverse types of omics data—genomics, transcriptomics, proteomics, and metabolomics. Omics data is valuable for uncovering new biomarkers and insights into cancer, but it is challenging to analyze due to its high dimensionality and noise. Our comparative analysis involves preprocessing steps such as normalization, imputation, and feature selection, and evaluates model performance using metrics like accuracy, precision, recall, F1-score, and AUC-ROC. Results indicate that deep learning models generally outperform traditional methods, with each model demonstrating unique strengths: CNNs excel at spatial pattern recognition, RNNs are effective with time-series data, DBNs integrate multi-omics data well, and Autoencoders are adept at dimensionality reduction. Case studies highlight these models’ potential to improve cancer diagnosis, predict patient outcomes, and identify novel therapeutic targets. Despite their promise, challenges remain, including data quality, model interpretability, generalizability, and computational demands. Future research should focus on enhancing model transparency, expanding datasets, and optimizing computational efficiency. Overall, this study underscores the transformative potential of deep learning in cancer research and the need for interdisciplinary collaboration to advance precision oncology.