Ovarian cancer remains one of the leading causes of cancer-related mortality among women due to its asymptomatic nature in early stages and lack of effective screening methods. Early diagnosis significantly improves the prognosis and survival rates of patients, but current diagnostic methods, such as imaging and biomarkers, are often limited in sensitivity and specificity. In recent years, machine learning (ML) algorithms have shown promise in enhancing early detection and diagnosis of ovarian cancer by analyzing complex datasets and uncovering hidden patterns that are often overlooked by traditional methods. This review examines the application of various machine learning techniques, including supervised learning (such as support vector machines, decision trees, and random forests) and unsupervised learning (such as clustering algorithms), in the early detection of ovarian cancer. We discuss the role of ML in analyzing medical images (e.g., ultrasound, MRI), histopathological data, genetic information, and biomarker levels to identify malignant ovarian tumors at their earliest stages. Additionally, the review highlights the challenges associated with integrating ML-based approaches in clinical practice, including data quality, model interpretability, and validation. Despite these challenges, the potential of machine learning in transforming ovarian cancer diagnosis is evident, offering an opportunity for more accurate, timely, and non-invasive detection. We conclude that the continued development and validation of ML algorithms could significantly contribute to improving the early diagnosis and personalized treatment of ovarian cancer, ultimately improving patient outcomes and survival rates.