Groundwater is a fundamental resource supporting agriculture, industry, and the drinking water supply, particularly in areas characterized by scarce surface water availability. However, groundwater systems face growing threats from over-extraction, pollution, and climate change. This chapter offers an extensive bibliometric analysis and critical evaluation of the research on groundwater vulnerability, using the Scopus database and advanced analysis via Biblioshiny. Between 2004 and 2024, the research on groundwater vulnerability gained growing interest globally, with a surge of documents published in just two decades (an annual growth rate of 10.37%). International collaboration was robust, with 28% of publications involving co-authors from multiple countries, and the average citation rate reached 25.76 citations per publication. The analysis reveals a shift from traditional index-based models, such as DRASTIC and SINTACS, toward hybrid approaches that integrate multi-criteria decision analysis and artificial intelligence. Machine learning techniques, including random forests, have achieved high predictive accuracy, with recent case studies reporting area under the curve values exceeding 0.95 and strong correlations between model outputs and contaminant levels. Despite these advancements, challenges persist, including data scarcity, model transparency, and the need for better integration of socioeconomic and governance factors. The chapter discusses the sustainable implications of vulnerability assessment, linking research outputs to social, economic, and environmental pillars, and relevant sustainable development goals (SDGs). By synthesizing quantitative bibliometric findings with technical review and case study evidence, this study provides a strategic foundation for advancing groundwater vulnerability assessment and supporting robust, data-driven management strategies in a changing world.