This systematic review explores the role of deep learning (DL) in improving the diagnostic accuracy of ovarian cancer using various medical imaging modalities. The study methodology involved establishing research questions, inclusion and exclusion criteria, and a comprehensive search strategy across relevant databases. The selected studies focused on DL techniques applied to ovarian cancer diagnosis, tumor differentiation, and radiomics. DL, particularly convolutional neural networks (CNNs), has emerged as a promising solution to enhance the accuracy of ovarian cancer detection. The review highlights the potential of DL in accelerating the diagnostic process and offering more precise and efficient solutions. DL models have demonstrated comparable diagnostic performance to experienced radiologists in categorizing ovarian tissues and achieving high areas under the receiver operating characteristic curve (AUC). The integration of DL into ovarian cancer diagnosis holds significant promise for improving patient outcomes, refining treatment approaches, and supporting informed decision-making. However, additional research and validation are necessary to ensure the reliability and applicability of DL models in clinical settings. The review covers various DL architectures, such as U-Net, ResNet, and YOLOv5, and evaluates their performance in differentiating between benign and malignant ovarian tumors using MRI, ultrasound, CT, and PET images. Studies show that DL models can achieve high accuracy, precision, recall, and F1 scores, surpassing other segmentation algorithms and providing valuable insights for patient staging and treatment decisions. Overall, the review underscores the potential of DL in advancing the field of ovarian cancer diagnosis, but emphasizes the need for further research to validate and refine these models for broader clinical application.This systematic review explores the role of deep learning (DL) in improving the diagnostic accuracy of ovarian cancer using various medical imaging modalities. The study methodology involved establishing research questions, inclusion and exclusion criteria, and a comprehensive search strategy across relevant databases. The selected studies focused on DL techniques applied to ovarian cancer diagnosis, tumor differentiation, and radiomics. DL, particularly convolutional neural networks (CNNs), has emerged as a promising solution to enhance the accuracy of ovarian cancer detection. The review highlights the potential of DL in accelerating the diagnostic process and offering more precise and efficient solutions. DL models have demonstrated comparable diagnostic performance to experienced radiologists in categorizing ovarian tissues and achieving high areas under the receiver operating characteristic curve (AUC). The integration of DL into ovarian cancer diagnosis holds significant promise for improving patient outcomes, refining treatment approaches, and supporting informed decision-making. However, additional research and validation are necessary to ensure the reliability and applicability of DL models in clinical settings. The review covers various DL architectures, such as U-Net, ResNet, and YOLOv5, and evaluates their performance in differentiating between benign and malignant ovarian tumors using MRI, ultrasound, CT, and PET images. Studies show that DL models can achieve high accuracy, precision, recall, and F1 scores, surpassing other segmentation algorithms and providing valuable insights for patient staging and treatment decisions. Overall, the review underscores the potential of DL in advancing the field of ovarian cancer diagnosis, but emphasizes the need for further research to validate and refine these models for broader clinical application.