Generative artificial intelligence (GAI) is transforming hydrogeological and environmental research, yet systematic evaluation of its reliability, appropriate applications, and risks is underreported. This chapter renders the first in-depth assessment across various hydrogeological subdisciplines, synthesizing capabilities, documenting failure modes, and establishing frameworks for responsible integration. It reveals that GAI proves genuinely useful yet fundamentally unreliable without rigorous verification. Code generation provably speeds workflows, with high success rates, enabling practitioners to automate data processing, create visualizations, and develop preliminary numerical models more efficiently. Also, the integration of GAI with machine learning approaches creates promising hybrid systems for groundwater prediction and water quality assessment, while communication capabilities enhance stakeholder engagement by translating technical findings into accessible formats. However, critical limitations demand caution. While recent models achieve below 1% hallucination rates on general conversational tasks, citation-specific applications central to literature review exhibit error rates up to 91%. Several physical inconsistency failures have been noted in the use of GAIs for genuine understanding of groundwater characteristics. In alignment with sustainable use, human expertise is found to be irreplaceable for brainstorming, conceptual model development, field observation interpretation, and ethical judgment about competing water uses. The GAI technology functions optimally as a supervised assistant for well-defined tasks such as preliminary literature exploration, routine coding, communication drafting, not for consequential technical decisions that can affect drinking water protection or aquifer sustainability. This chapter includes frameworks for verification protocols, prompt engineering strategies, and integrating guidelines for sustainable practice. For educators, policymakers, and professional organizations, pathways for navigating between innovation and precaution have been outlined. The chapter equips the global community with evidence-based guidance for harnessing genuine utility while protecting against risks in an era where analytical errors carry consequences for human health and environmental sustainability.