The compliance with environmental regulations creates a major burden for manufacturing companies due to challenges such as limited skilled personnel, tight project timelines, complexity, changes in regulations, and development of new technologies. Artificial Intelligence (AI) presents an opportunity to address these barriers by automating repetitive tasks, enabling professionals to focus on critical thinking, intuition, and judgment. This research explores the integration of AI for review of environmental regulations using advanced Large Language Models (LLMs) combined with Retrieval-Augmented Generation (RAG) for domain-specific knowledge application. The proposed AI systems leverage historical data and machine learning to generate insights and support decision-making with minimal human intervention. A targeted approach to training AI on curated, facility-specific datasets ensures reliable outputs, fostering user trust and facilitating effective decision-making based on review of environmental regulations. Additionally, incorporating an AI agentic workflow empowers systems to operate autonomously, proactively addressing compliance challenges, adapting to evolving regulatory landscapes, and offering actionable recommendations with minimal reliance on human oversight.