Depression remains a major global mental health problem, affecting predominantly women as opposed to men. Early identification and intervention depend on the development of reliable predictive tools. Using statistical methods, machine learning (ML), and multivariate approaches, this work presents a comprehensive survey of current methods to predict depression in women. Ko, J. Y., et al. (2020). Statistical models such as logistic regression focus on genetic, behavioral, and environmental factors, while clinical, demographic, and psychological data are used to improve predictive accuracy with machine learning models such as Extreme and Extreme Randomized Trees. Comprehensive assessment of depression severity is performed using multiple methods, incorporating multiple sources of data, including social media activity, facial expressions, and physiological symptoms (e.g., EEG). Despite many improvements, these models still have significant shortcomings. Generalization is hindered by data biases, including the underrepresentation of certain populations and the use of retrospective data. Model reliability is hindered by methodological problems such as overfitting, lack of final validation, and interpretability issues (Zhou, X., et al., 2021). Practical obstacles, such as limited integration into health care systems, limited data quality, and privacy concerns, further hinder global adoption. In addition, many models do not account for women-specific risk factors, such as intergenerational transmission, hormonal influences, and postnatal life stages. These gaps must be addressed to develop accurate and effective prediction methods. To improve the diagnosis of depression in women and fill gaps in current research, this review highlights the need for more real-time predictions, more diverse data, and more accessible methods for clinicians (Zhou, X., et al., 2021).