Deep learning (DL) is transforming cancer genomics and histopathology by enabling the extraction of actionable insights from large datasets. In precision oncology, DL is used to analyze histopathological and genomic data, improving diagnostic accuracy and supporting personalized treatment decisions. DL models can predict tumor diagnosis, subtype, grading, prognosis, and treatment response, while also identifying genetic alterations and biomarkers. These models are trained on large datasets, including histopathological images and genomic data, and can integrate multimodal information for enhanced performance. In histopathology, DL is used for tasks such as tumor detection, subtyping, and grading. Weakly supervised DL models, which do not require manual annotations, are particularly useful for large-scale data analysis. These models can predict tumor presence, survival, and other clinical outcomes. In genomics, DL is applied to predict mutations, driver genes, and drug responses, often using RNA-sequencing and other molecular data. DL has also been used to predict cancer outcomes based on DNA methylation, miRNA, and other biomarkers. Multimodal DL models combine histopathological and genomic data to improve diagnostic accuracy and clinical outcomes. These models can integrate data from multiple sources, including imaging, genomics, and clinical records, to provide more comprehensive insights. However, challenges remain in terms of data quality, model generalizability, and clinical implementation. DL models require large, diverse datasets and must be validated for accuracy and reliability. Additionally, ethical considerations, such as data bias and model interpretability, are important for ensuring fair and effective use of DL in healthcare. Despite these challenges, DL is showing great promise in improving cancer diagnosis and treatment. As DL technology continues to evolve, it is expected to play an increasingly important role in precision oncology, enabling more personalized and effective treatment strategies.Deep learning (DL) is transforming cancer genomics and histopathology by enabling the extraction of actionable insights from large datasets. In precision oncology, DL is used to analyze histopathological and genomic data, improving diagnostic accuracy and supporting personalized treatment decisions. DL models can predict tumor diagnosis, subtype, grading, prognosis, and treatment response, while also identifying genetic alterations and biomarkers. These models are trained on large datasets, including histopathological images and genomic data, and can integrate multimodal information for enhanced performance. In histopathology, DL is used for tasks such as tumor detection, subtyping, and grading. Weakly supervised DL models, which do not require manual annotations, are particularly useful for large-scale data analysis. These models can predict tumor presence, survival, and other clinical outcomes. In genomics, DL is applied to predict mutations, driver genes, and drug responses, often using RNA-sequencing and other molecular data. DL has also been used to predict cancer outcomes based on DNA methylation, miRNA, and other biomarkers. Multimodal DL models combine histopathological and genomic data to improve diagnostic accuracy and clinical outcomes. These models can integrate data from multiple sources, including imaging, genomics, and clinical records, to provide more comprehensive insights. However, challenges remain in terms of data quality, model generalizability, and clinical implementation. DL models require large, diverse datasets and must be validated for accuracy and reliability. Additionally, ethical considerations, such as data bias and model interpretability, are important for ensuring fair and effective use of DL in healthcare. Despite these challenges, DL is showing great promise in improving cancer diagnosis and treatment. As DL technology continues to evolve, it is expected to play an increasingly important role in precision oncology, enabling more personalized and effective treatment strategies.