The integration of Artificial intelligence (AI) in hydrogeological research is revolutionizing how we predict and protect water resources yet demands a parallel commitment towards ethical awareness of such data driven innovation. To assess the ongoing applications in hydrogeological field, the study was conducted to derive the future scope and associated ethical concerns arising in the recent decade. The findings suggest that implementation of machine learning (ML) and deep learning (DL) algorithms are at forefront of this field, with majority of publications related to water quality, water quantity management, statistical assessment and health risk assessments in the past 15 years. Data intensive algorithms such as Artificial neural networks (ANN), Random Forest (RF), and Fuzzy inferences some of the most commonly used in this field and have reduced the error levels more than 30% especially in case of water quality models. The implementation of algorithms such as TabNet, TabTransformer, MLP, CatBoost, and AdaBoost have potential to thrive in data scarce region in nonconventional manner with help of tools like GeoAI. Additionally, the novel approaches such as integration of ANNs and techniques like Monte Carlo Simulation has potential of revolutionize the way we conceptualize the health risk assessment in the current scenario with real time data input. On contrary, such advancements have come with the costs of common and critical violation in a multispectral manner such as data privacy and ownerships, transparency of data-based algorithms, marginalization of local entities and environmental manipulation, and at foremost disintegration of academic ethics especially in post-graduate research. The future of AI driven hydrogeological research should focus on strengthening academic integrity at base level by implementation of AI literacy in research and should extend towards AI adaptability, AI model realism and policy implementation for data accountability.