The worldwide water supply depends heavily on groundwater yet this vital resource remains defenseless against climate change intensification. The investigation combines the analysis of climate change effects on groundwater systems with modern technological assessments of remote sensing along with Geographic Information Systems (GIS) and big data analytics and cloud computing and Artificial Intelligence (AI) and Machine Learning (ML) to boost groundwater sustainability. The study utilizes VoSviewer to analyze trends in scholarly literature from 2000 to 2024 and networks between authors, co-citations and performance indicators via Field Citation Ratio and Relative Citation Ratio based on a bibliometric review of this time period. The research shows that interdisciplinary studies using technology have dramatically increased their focus on addressing groundwater stress criteria. The research pinpoints that arid and semi-arid zones represent specific regional hotspot areas because climate change impacts recharge patterns and leads to increased salinization as well as heavy aquifer extraction. The combination of artificial intelligence modeling and remote sensing groundwater mapping together with IoT-enabled sensors provides essential services for monitoring and prediction as well as adaptive management systems. The progress of sustainable groundwater management is constrained because institutions block its implementation and some regions lack equal access to technology and their data exists in isolated pieces. The research demonstrates the need to combine digital tools in groundwater policy structures alongside international cooperative efforts to establish long-lasting hydro-governance infrastructure. The presented work provides essential data-supported foundations that lead to sustainable groundwater management policies under conditions of climate change pressure.