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Simple Summary: Ovarian cancer is one of leading cause of death among women and early detection is important for timely treatment. For its detection at early stages, machine learning can be significantly important to speed up the screening and provide more accurate results. A stacked model is designed in this study, combining the strengths of mutliple models to obtain better accuracy compared to existing models. With a 96.87% accuracy, the model proves to be robust and accurate. The use of explainable AI elaborates on the importance of various appropriate features to enhance cancer detection accuracy in this regard. The importance of detecting and preventing ovarian cancer is of utmost significance for women's overall health and wellness. Referred to as the "silent killer," ovarian cancer exhibits inconspicuous symptoms during its initial phases, posing a challenge for timely identification. Identification of ovarian cancer during its advanced stages significantly diminishes the likelihood of effective treatment and survival. Regular screenings, such as pelvic exams, ultrasound, and blood tests for specific biomarkers, are essential tools for detecting the disease in its early, more treatable stages. This research makes use of the Soochow University ovarian cancer dataset, containing 50 features for the accurate detection of ovarian cancer. The proposed predictive model makes use of a stacked ensemble model, merging the strengths of bagging and boosting classifiers, and aims to enhance predictive accuracy and reliability. This combination harnesses the benefits of variance reduction and improved generalization, contributing to superior ovarian cancer prediction outcomes. The proposed model gives 96.87% accuracy, which is currently the highest model result obtained on this dataset so far using all features. Moreover, the outcomes are elucidated utilizing the explainable artificial intelligence method referred to as SHAPly. The excellence of the suggested model is demonstrated through a comparison of its performance with that of other cutting-edge models. [ABSTRACT FROM AUTHOR]