Ovarian cancer continues to be one of the most lethal cancers affecting the female reproductive system due to late-stage diagnosis and limited effective screening methods. Traditional diagnostic methods for ovarian cancer typically rely on imaging, histopathological analysis, and biomarker detection, which can be slow and prone to human error. Recent developments in the areas of Artificial Intelligence (AI) & Machine Learning (ML) collectively have demonstrated significant role in improving precision as well as efficiency while detecting and classifying ovarian cancer. The study in this paper explores the role of AI models that have been pre-trained, particularly deep learning architectures, to analyze various types of medical data, including histopathological images, radiological scans, and genomic information. By utilizing transfer learning, these models can efficiently extract important features from large datasets and adapt them for ovarian cancer classification with minimal computational demands. The suggested framework integrates Convolutional Neural Networks (CNNs) for analyzing images with conventional machine learning techniques to process genomic and clinical data. Experimental findings show that pretrained models significantly boost diagnostic accuracy, offering improved sensitivity and specificity over traditional approaches. Furthermore, methods like Grad-CAM have been employed so that it can increase the model’s transparency, aiding its acceptance in clinical settings. This study highlights the promise of AI-driven diagnostic tools in supporting detection of the disease early & tailored treatment for ovarian cancer. Findings of this paper highlight importance of integrating deep learning with specialized domain knowledge to improve diagnostic precision while reducing false positives and negatives. Future research will focus on expanding datasets,