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Data science is an emerging subject that integrates traditional scientific research with data mining, machine learning, and massive datasets. The healthcare sector produces copious amounts of valuable data on patient demographics, treatment plans, diagnostic outcomes, financial coverage, and more. The healthcare business generates a great deal of data, much of it unstructured and in need of processing, management, analysis, and integration. Results can only be trusted if the data has been managed and analysed effectively. This article explores into healthcare-specific applications of data cleansing, data mining, data preparation, and data analysis. This article offers a glance into the present and future prospects of big data analytics in healthcare by outlining the advantages, describing the frameworks and technique used, analysing the current obstacles encountered, and proposing potential solutions. The primary goal of this work is to evaluate and map data science approaches used to improve women's health, as well as to investigate the nature and efficacy of the issues that have been addressed. Data science techniques, such as text analytics, science mapping, and descriptive assessment, are frequently employed in investigations into women's health. The application of data science techniques to women's health issues is an exciting new field of study since it has the potential to be both effective and cost-efficient. Women bear a disproportionate share of the health consequences of the current COVID-19 pandemic. More needs to be done by policymakers to ensure women have equal access to health care.